Foreword from the Congress Chairs
For the Turing year 2012, AISB (The Society for the Study of Artificial
Intelligence and Simulation of Behaviour) and IACAP (The International
Association for Computing and Philosophy) merged their annual
symposia/conferences to form the AISB/IACAP World Congress. The
congress took place 2–6 July 2012 at the University of Birmingham, UK.
The Congress was inspired by a desire to honour Alan Turing, and by
the broad and deep significance of Turing's work to AI, the philosophical
ramifications of computing, and philosophy and computing more generally.
The Congress was one of the events forming the Alan Turing Year.
The Congress consisted mainly of a number of collocated Symposia on
specific research areas, together with six invited Plenary Talks. All papers
other than the Plenaries were given within Symposia. This format is perfect
for encouraging new dialogue and collaboration both within and between
research areas.
This volume forms the proceedings of one of the component symposia.
We are most grateful to the organizers of the Symposium for their hard work
in creating it, attracting papers, doing the necessary reviewing, defining an
exciting programme for the symposium, and compiling this volume. We
also thank them for their flexibility and patience concerning the complex
matter of fitting all the symposia and other events into the Congress week.

John Barnden (Computer Science, University of Birmingham)
Programme Co-Chair and AISB Vice-Chair
Anthony Beavers (University of Evansville, Indiana, USA)
Programme Co-Chair and IACAP President
Manfred Kerber (Computer Science, University of Birmingham)
Local Arrangements Chair

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

3

Foreword from the Workshop Chairs
2010 marked the 60th anniversary of the publication of Turing’s paper, in
which he outlined his test for machine intelligence. Turing suggested that
consideration of genuine machine thought should be replaced by use of a
simple behaviour-based process in which a human interrogator converses
blindly with a machine and another human. Although the precise nature of
the test has been debated, the standard interpretation is that if, after five
minutes interaction, the interrogator cannot reliably tell which respondent is
the human and which the machine then the machine can be qualified as a
'thinking machine'. Through the years, this test has become synonymous as
'the benchmark' for Artificial Intelligence in popular culture.
There is both widespread dissatisfaction with the 'Turing test' and
widespread need for intelligence testing that would allow to direct AI
research towards general intelligent systems and to measure success. There
are a host of test beds and specific benchmarks in AI, but there is no
agreement on what a general test should even look like. However, this test
seems exceedingly useful for the direction of research and funding. A
crucial feature of the desired intelligence is to act successfully in an
environment that cannot be fully predicted at design time, i.e. to produce
systems that behave robustly in a complex changing environment - rather
than in virtual or controlled environments. The more complex and changing
the environment, however, the harder it becomes to produce tests that allow
any kind of benchmarking. Intelligence testing is thus an area where
philosophical analysis of the fundamental concepts can be useful for cutting
edge research.
There has been recently a growing interest in simulating and testing in
machines not just intelligence, but also other mental human phenomena, like
qualia. The challenge is twofold: the creation of conscious artificial systems,
and the understanding of what human consciousness is, and how it might
arise. The appeal of the Turing Test is that it handles an abstract inner
process and renders it an observable behaviour, in this way, in principle; it
allows us to establish a criteria with which we can evaluate technological
artefacts on the same level as humans.
New advances in cognitive sciences and consciousness studies suggest it
may be useful to revisit this test, which has been done through number of
symposiums and competitions. However, a consolidated effort has been
attempted in 2010 and in 2011 at AISB Conventions through TCIT
symposiums. However, this year’s symposium forms the consolidated effort
of a larger group of researchers in the field of machine intelligence to
revisit, debate, and reformulate (if possible) the Turing test into a
comprehensive intelligence test that may more usefully be employed to
evaluate 'machine intelligence' at during the 21st century.

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

4

The Chairs
Vincent C. Müller (Anatolia College/ACT & University of Oxford) and
Aladdin Ayesh (De Montfort University)
With the Support of:
Mark Bishop (Goldsmiths, University of London),
John Barnden (University of Birmingham),
Alessio Plebe (University Messina) and
Pietro Perconti (University Messina)
The Program Committee:
Raul Arrabales (Carlos III University of Madrid),
Antonio Chella (University of Palermo),
Giuseppe Trautteur (University of Napoli Federico II),
Rafal Rzepka (Hokkaido University)
… plus the Organizers Listed Above

The website of our symposium is on http://www.pt-ai.org/turing-test

Cite as:
Müller, Vincent C. and Ayesh, Aladdin (eds.) (2012), Revisiting Turing and
his Test: Comprehensiveness, Qualia, and the Real World (AISB/IACAP
Symposium) (Hove: AISB).
Surname, Firstname (2012), ‘Paper Title’, in Vincent C. Müller and Aladdin
Ayesh (eds.), Revisiting Turing and his Test: Comprehensiveness, Qualia,
and the Real World (AISB/IACAP Symposium) (Hove: AISB), xx-xx.

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

5

Table of Contents
Foreword from the Congress Chairs

3

Foreword from the Workshop Chairs

4

Daniel Devatman Hromada
From Taxonomy of Turing Test-Consistent Scenarios Towards
Attribution of Legal Status to Meta-modular Artificial
Autonomous Agents

7

Michael Zillich
My Robot is Smarter than Your Robot: On the Need for a Total
Turing Test for Robots

12

Adam Linson, Chris Dobbyn and Robin Laney
Interactive Intelligence: Behaviour-based AI, Musical HCI and
the Turing Test

16

Javier Insa, Jose Hernandez-Orallo, Sergio España,
David Dowe and M.Victoria Hernandez-Lloreda
The anYnt Project Intelligence Test (Demo)

20

Jose Hernandez-Orallo, Javier Insa, David Dowe
and Bill Hibbard
Turing Machines and Recursive Turing Tests

28

Francesco Bianchini and Domenica Bruni
What Language for Turing Test in the Age of Qualia?

34

Paul Schweizer
Could there be a Turing Test for Qualia?

41

Antonio Chella and Riccardo Manzotti
Jazz and Machine Consciousness: Towards a New Turing Test

49

William York and Jerry Swan
Taking Turing Seriously (But Not Literally)

54

Hajo Greif
Laws of Form and the Force of Function: Variations on the
Turing Test

60

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

6

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AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

7

3,4#($# ('&.-&C+*2')6 #Q-& #.3&$%'&#,*)(,&+$,-*# 2-C,*4#.&-C# $%,)#
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ETT
infix
!"
BA
SE

Intelligence
group
Corporal
group
Babbling
group
Sensual
group

Subordinated
intelligence types
Organic; Spatial;
Somato-sexual
Moral; Emotional;
Linguistic
Mathematico-logical;
Musical; Visual

Table 2. Clustering of basic intelligence types into basic
intelligence clusters
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AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

10

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AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

11

My Robot is Smarter than Your Robot - On the Need for
a Total Turing Test for Robots
Michael Zillich1
Abstract. In this position paper we argue for the need of a Turinglike test for robots. While many robotic demonstrators show impressive, but often very restricted abilities, it is very difficult to assess
how intelligent such a robot can be considered to be. We thus propose a test, comprised of a (simulated) environment, a robot, a human
tele-operator and a human interrogator, that allows to assess whether
a robot behaves as intelligently as a human tele-operator (using the
same sensory input as the robot) with respect to a given task.

1

INTRODUCTION

The Turing Test [35] considered the equivalent of a brain in a vat,
namely an AI communicating with a human interrogator solely via
written dialogue. Though this did not preclude the AI from having acquired the knowledge that it is supposed to display via other means,
for example extended multi-sensory interactions within a complex
dynamic environment, it did narrow down what is considered as relevant for the display of intelligence.
Intelligence however encompasses more than language. Intelligence, in all its flavours, developed to provide a competitive advantage in coping with a world full of complex challenges, such as moving about, manipulating things (though not necessarily with hands),
hiding, hunting, building shelter, caring for offspring, building social contacts, etc. In short, intelligence needs a whole world to be
useful in, which prompted Harnad to propose the Total Turing Test
[19], requiring responses to all senses not just formatted linguistic
input. Note that we do not make an argument here about the best approach to explain the emergence of intelligence (though we consider
it likely that a comprehensive embodied perspective will help), but
only about how to measure intelligence without limiting it to only a
certain aspect.
The importance of considering all aspects of intelligence is also
fully acknowledged in robotics, where agents situated in the real
world are faced with a variety of tasks, such as navigation and map
building, object retrieval, or human robot interaction, which require
various aspects of intelligence in order to be successfully carried out
in spite of all the challenges of complex and dynamic scenes. So
robotics can serve as a testbed for many aspects of intelligence. In
fact it is the more basic of the above aspects of intelligence that still
pose major difficulties. This is not to say that there was no progress
over the years. In fact there are many impressive robot demonstrators now displaying individual skills in specific environments, such
as bipedal walking in the Honda Asimo [6] or quadruped walking in
the Boston Dynamics BigDog[32], learning to grasp [25, 33], navigation in the Google Driverless Car or even preparing pancakes [11].
For many of these demonstrators however it is easy to see where
1

Vienna University of Technology, Austria, email: zillich@acin.tuwien.ac.at

the limitations lie and typically the designers are quick to admit that
this sensor placement or that choice of objects was a necessary compromise in order to concentrate on the actually interesting research
questions at hand.
This makes it difficult however to quantitatively compare the performance of robots. Which robot is smarter, the pancake-flipping
robot in [11]2 , the beer-fetching PR23 or the pool-playing PR24 ?
We will never know.
A lot of work goes into these demonstrators, to do several runs
at conferences or fairs and shoot videos, before they are shelved or
dismantled again, but it is often not clear what was really learned in
the end; which is a shame, because certainly some challenges were
met with interesting solutions. But the limits of these solutions were
not explored within the specific experimental setup of the demo.
So what we argue for is a standardised, repeatable test for complete robotic systems. This should test robustness in basic “survival”
skills, such as not falling off stairs, running into mirrors or getting
caught in cables, as well as advanced tasks, such as object search,
learning how to grasp or human-robot interaction including natural
language understanding.

2

RELATED WORK

2.1 Robot Competitions
Tests are of course not new in the robotics community. There are
many regular robot challenges which have been argued to serve
as benchmarks [12], such as RoboCup [24] with its different challenges (Soccer, Rescue, @Home), the AAAI Mobile Robot Competitions [1], or challenges with an educational background like the
US FIRST Robotics Competitions [8] or EUROBOT [3]. Furthermore there are specific targeted events such as the DARPA Grand
Challenges 2004 and 2005 and DARPA Urban Challenge 2007 [2].
While these events present the state of the art and highlight particularly strong teams, they only offer a snapshot at a particular point in
time. And although these events typically provide a strict rule book,
with clear requirements and descriptions of the scenarios, the experiments are not repeatable and the test arena will be dismantled after
the event (with the exception of simulations of course). So while offering the ultimate real-world test in a challenging and competitive
setting, and thus providing very important impulses for robotics research, these tests are not suitable because a) they are not repeatable,
b) rules keep changing to increase difficulty and maintain a challenging competition and c) the outcomes depend a lot on factors related
2 www.youtube.com/watch?v=4usoE981e7I
3 www.willowgarage.com/blog/2010/07/06/beer-me-robot
4 www.willowgarage.com/blog/2010/06/15/pr2-plays-pool

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to the team (team size and funding, quality of team leadership) rather
than the methods employed within the robot.

2.2 Robotic Benchmarks
The robotics community realised the need for repeatable quantitative
benchmarks [15, 21, 26, 27], leading to a series of workshops, such as
the Performance Metrics for Intelligent Systems (PerMIS) or Benchmarks in Robotics Research or the Good Experimental Methodology
in Robotics series, and initiatives such as the EURON Benchmarking Activities [4] or the NIST Urban Search And Rescue (USAR)
testbed [7].
Focusing on one enabling capability at a time, some benchmarks
concentrate on path planning [10], obstacle avoidance [23], navigation and mapping [9, 13], visual servoing [14], grasping [18, 22] or
social interaction [34, 20]. Taking into account whole robotic systems [16] propose benchmarking biologically inspired robots based
on pursuit/evasion behaviour. Also [29] test complete cognitive systems in a task requiring to find feeders in a maze and compete with
other robots.

2.3 Robot Simulators
Robotics has realised the importance of simulation environments
early on, and a variety of simulators exist. One example is
Player/Stage [17], a robot middleware framework and 2D simulation
environment intended mostly for navigation tasks and its extension
to a full 3D environment with Gazebo [5], which uses a 3D physics
engine to simulate realistic 3D interactions such as grasping and
has recently been chosen as the simulation test bed for the DARPA
Robotics Challenge for disaster robots. [28] is another full 3D
simulator, used e.g. for simulation of robotic soccer players. Some
simulators such as [30] and [36] are specialised to precise simulation of robotic grasping. These simulators are valuable tools for
debugging specific methods, but their potential as a common testbed
to evaluate complete robotic systems in a set of standardised tasks
has not been fully explored yet.
In summary, we have on the one hand repeatable, quantitative benchmarks mostly tailored to sub-problems (such as navigation or grasping) and on the other hand competitions testing full systems at singular events, where both of these make use of a mixture of simulations
and data gathered in the real world.

3

THE TOTAL TURING TEST FOR ROBOTS

What has not fully emerged yet however is a comprehensive test suite
for complete robotic systems, maintaining a clearly specified test environment plus supporting infrastructure for an extended period of
time, allowing performance evaluation and comparison of different
solutions and measuring their evolution over time is What this test
suite should assess is the overall fitness of a robotic system to cope
with the real world and behave intelligently in the face of unforeseen
events, incomplete information etc. Moreover the test should ideally
convey its results in an easily accessible form also to an audience
beyond the robotics research community, allowing other disciplines
such as Cognitive Science and Philosophy as well as the general public to assess progress of the field, beyond eye-catching but often shallow and misleading demos,
Harnads [19] Total Turing Test provides a fitting paradigm, requiring that “The candidate [the robot] must be able to do, in the real

world of objects and people, everything that real people can do, in a
way that is indistinguishable (to a person) from the way real people
do it.”
“Everything” will of course have to be broken down into concrete
tasks with increasing levels of difficulty. And the embodiment of the
robot will place constraints on the things it can do in the real world,
which has to be taken into account accordingly.

3.1 The Test
The test would consist of a given scene and a set of tasks to be performed by either an autonomous robot or a human tele-operating a
robot (based on precisely the same sensor data the robot has available, such as perhaps only a laser ranger and bumpers). A human interrogator would assign tasks to the robot, and also place various obstacles that interfere with successful completion. If the human interrogator can not distinguish the performance of the autonomous robot
from the performance of the tele-operated robot, the autonomous
robot can be said to be intelligent, with respect to the given task.
Concretely the test would have to consist of a standardised environment with a defined set of tasks, as is e.g. common in the
RoboCup@Home challenges (fetch an item, follow a user). The test
suite would provide a API, e.g. based on the increasingly popular
Robot Operating System (ROS) [31], allowing each robot to be connected to it, with moderate effort. Various obstacles and events could
be made to interfere with execution of these tasks, such as cables
lying on the floor, closed glass doors, stubborn humans blocking
the way. Different challenges will pose different problems for different robots. E.g. for the popular omnidirectional drives of holonomic
bases such as the Willow Garage PR2 cables on the floor represent
insurmountable obstacles, while other robots will have difficulties
navigating in tight environments.

3.2 Simulation
A basic building block for such a test suite is an extension of available
simulation systems to allow fully realistic simulation of all aspects of
robotic behaviour.
The simulation environment would have to provide photo-realistic
rendering with accurate noise models (such as lens flares or poor dynamic range as found in typical CCD cameras) beyond the visually
pleasing but much to “clean” rendering of available simulators. Also
the physics simulation will have to be very realistic, which means
that the simulation might not be able to run in real time. Real time
however is not necessarily a requirement for a simulation as long as
computation times of employed methods are scaled in accordance.
Furthermore the simulation would need to also contain humans, instructing the robot in natural language, handing over items or posing
as dynamic obstacles for navigation.
Figure 1 shows a comparison of a robot simulated (and in this
case tele-operated) in a state of the art simulator (gazebo) with the
corresponding real robot carrying out the same task autonomously as
part of a competition [37]. While the simulation could in this case
provide reasonably realistic physics simulation (leading to objects
slipping out of the hand if not properly grasped) and simulation of
sensors (to generate e.g. problems for stereo reconstruction in lowtexture areas) more detailed simulations will be needed to capture
more aspects of the real world.

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(a)

(b)

Figure 2. Example score for a fictional robot equipped with a laser ranger
and camera, but no arm and language capabilities. Figures are scores on the
Pass/fail test and Intelligence test respectively.

Figure 1. Comparison of (tele-operated) simulation and (autonomous) real
robot in a fetch and carry task.

3.3 Task and Stages
The test would be set up in different tasks and stages. Note that we
should not require a robot to do everything that real people can do
(as originally formulated by Harnad). Robots are after all designed
for certain tasks, requiring only a specific set of abilities (capable of
language understanding, equipped with a gripper, ability to traverse
outdoor terrain, etc.). And we are interested in their capabilities related to these tasks. The constraints of a given robot configuration
(such as the ability to understand language) then apply to the robot
as well as the human tele-operator.
Stages would be set up with increasing difficulties, such that a
robot can be said to be stage-1 safe for the fetch and carry task (all
clean, static environment) but failing stage 2 in 20% of cases (e.g.
unforeseen obstacles, changing lighting). The final stages would be
a real world test in a mock-up constructed to follow the simulated
world. While the simulation would be a piece of software available
for download, the real world test would be held as an annual competition much like RoboCup@Home, with rules and stages of difficulty
according to the simulation. Note that unlike in RoboCup@Home
these would remain fixed, rather than change with each year.

3.4 Evaluation
The test would then have two levels of evaluation.
Pass/fail test This evaluation would simply measure the percentage
of runs where the robot successfully performs a task (at a given
stage). This would be an automated assessment and allows developers to continuously monitor progress of their system.
Intelligence test This would be the actual Total Turing Test with humans interrogators assessing whether a task was performed (successfully or not) by a robot or human tele-operator. The score
would be related to the percentage of wrong attributions (i.e. robot
and tele-operator were indistinguishable). Test runs with human
tele-operators would be recorded once and stored for later comparison of provided robot runs. The requirement of collecting statistics from several interrogators means that this test is more elaborate and would be performed in longer intervals such as during
annual competitions. This evaluation then allows to assess the intelligence of a robot (with respect to a given task) in coping with
the various difficulties posed by a real environment.
The setup of tasks and stages allows to map the abilities of a given
robot. Figure 2 shows the scores of a fictional robot. The robot is
equipped with a laser ranger and camera and can thus perform the
navigation tasks as well as following a human, but lacks an arm for

carrying objects or opening doors as well as communication capabilities required for the human guidance task,
As can be seen the robot can be considered stage-1 intelligent with
respect to the random navigation task (driving around randomly without colliding or getting stuck), i.e. it is indistinguishable from a human tele-operator driving randomly, in the perfect simulated environment. It also achieves perfect success rates in this simple setting. Performance in the real world for perfect conditions (stage 4) is slightly
worse (the simulation could not capture all the eventualities of the
real world, such as wheel friction). Performance for added difficulties (such as small obstacles on the floor) decreases, especially in the
real word condition. Performance drops in particular with respect to
the tele-operator and so it becomes quickly clear to the interrogators
which is the robot and which the tele-operator, i.e. the robot makes
increasingly “stupid mistakes” such as getting stuck when there is
an obvious escape. Accordingly the intelligence score drops quickly.
The robot can also be said to be fairly stage-1 and stage-4 intelligent
with respect to navigation and human following, and slightly less intelligent with respect to finding objects.
In this respect modern vacuum cleaning robots (the more advanced
versions including navigation mapping capabilities) can be considered intelligent with respect to the cleaning task, as their performance there will generally match that of a human tele-operating such
a robot. For more advanced tasks including object recognition, grasping or dialogue the intelligence of most robots will quickly degrade
to 0 for any stages beyond 1.

4

CONCLUSION

We proposed a test paradigm for intelligent robotic systems, inspired
by Harnads Total Turing Test, that goes beyond current benchmarks
and robot competitions. This test would provide a pragmatic definition of intelligence for robots, as the capability to perform as good as
a tele-operating human for a given task. Moreover, test scores would
be a good indicator whether a robot is ready for the real world, i.e. is
endowed with enough intelligence to overcome unforeseen obstacles
and avoid getting trapped in “stupid” situations.
There are however several technical and organisational challenges
to be met. Running realistic experiments will require simulators of
considerably improved fidelity. But these technologies are becoming
increasingly available thanks in part to the developments in the gaming industry. Allowing researchers to simply plug in their systems
will require a careful design of interfaces to ensure that all capabilities are adequately covered. The biggest challenge might actually
be the definition of environments, tasks and stages. This will have
to be a community effort and draw on the experiences of previous
benchmarking efforts.

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[24] Hiroaki Kitano, Minoru Asada, Yasuo Kuniyoshi, Itsuki Noda, and Eiichi Osawa, ‘RoboCup: The Robot World Cup Initiative’, in IJCAI-95
workshop on entertainment and AI/ALife, (1995).
The research leading to these results has received funding from the
[25] D Kraft, N Pugeault, E Baseski, M Popovic, D Kragic, S Kalkan,
European Community’s Seventh Framework Programme [FP7/2007F Wörgötter, and N Krüger, ‘Birth of the object: Detection of object2013] under grant agreement No. 215181, CogX and from the Ausness and extraction of object shape through object action complexes’,
trian Science Fund (FWF) under project TRP 139-N23 InSitu.
International Journal of Humanoid Robotics, 5(2), 247–265, (2008).
[26] Raj Madhavan and Rolf Lakaemper, ‘Benchmarking and Standardization of Intelligent Robotic Systems’, Intelligence, (2009).
[27] Performance Evaluation and Benchmarking of Intelligent Systems, eds.,
REFERENCES
Raj Madhavan, Edward Tunstel, and Elena Messina, Springer, 2009.
[1] AAAI
Mobile
Robot
Competition,
[28] O. Michel, ‘Webots: Professional Mobile Robot Simulation’, Internahttp://www.aaai.org/Conferences/AAAI/2007/aaai07robot.php.
tional Journal of Advanced Robotic Systems, 1(1), 39–42, (2004).
[2] DARPA Grand Challenge, http://archive.darpa.mil/grandchallenge.
[29] Olivier Michel, Fabien Rohrer, and Yvan Bourquin, ‘Rat’s Life: A Cog[3] Eurobot, http://www.eurobot.org.
nitive Robotics Benchmark’, European Robotics Symposium, 223–232,
[4] EURON Benchmarking Initiative, www.robot.uji.es/EURON/en/index.html.
(2008).
[5] Gazebo 3D multi-robot simulator http://gazebosim.org.
[30] Andrew Miller and Peter K. Allen, ‘Graspit!: A Versatile Simulator for
[6] Honda ASIMO, http://world.honda.com/ASIMO.
Robotic Grasping’, IEEE Robotics and Automation Magazine, 11(4),
[7] NIST
Urban
Search
And
Rescue
(USAR),
110–122, (2004).
http://www.nist.gov/el/isd/testarenas.cfm.
[31] Morgan Quigley, Ken Conley, Brian Gerkey, Josh Faust, Tully Foote,
[8] US First Robotics Competition, www.usfirst.org.
Jeremy Leibs, Rob Wheeler, and Andrew Y Ng, ‘ROS: an open-source
[9] Benjamin Balaguer, Stefano Carpin, and Stephen Balakirsky, ‘ToRobot Operating System’, in ICRA Workshop on Open Source Software,
wards Quantitative Comparisons of Robot Algorithms: Experiences
(2009).
with SLAM in Simulation and Real World Systems’, in IROS Work[32] Marc Raibert, Kevin Blankespoor, Gabriel Nelson, Rob Playter, and
shop on Benchmarks in Robotics Research, (2007).
The BigDog Team, ‘BigDog, the Rough-Terrain Quadruped Robot’, in
[10] J Baltes, ‘A benchmark suite for mobile robots’, in Intelligent Robots
Proceedings of the 17th World Congress of The International Federaand Systems 2000IROS 2000 Proceedings 2000 IEEERSJ International
tion of Automatic Control, pp. 10822–10825, (2008).
Conference on, volume 2, pp. 1101–1106. IEEE, IEEE, (2000).
[33] Ashutosh Saxena, Justin Driemeyer, and Andrew Y. Ng, ‘Robotic
[11] Michael Beetz, Ulrich Klank, Ingo Kresse, Lorenz Maldonado, Alexis
Grasping of Novel Objects using Vision’, The International Journal
Mösenlechner, Dejan Pangercic, Thomas Rühr, and Moritz Tenorth,
of Robotics Research, 27(2), 157–173, (2008).
‘Robotic Roommates Making Pancakes’, in 11th IEEE-RAS Interna[34] Katherine M. Tsui, Munjal Desai, and Holly A. Yanco, ‘Towards Meational Conference on Humanoid Robots, (2011).
suring the Quality of Interaction: Communication through Telepresence
[12] S Behnke, ‘Robot competitions - Ideal benchmarks for robotics reRobots’, in Proceedings of the Performance Metrics for Intelligent Syssearch’, in Proc of IROS2006 Workshop on Benchmarks in Robotics
tems Workshop (PerMIS), (2012).
Research. Citeseer, (2006).
[35] Alan Turing, ‘Computing Machinery and Intelligence’, Mind, 59, 433–
[13] Simone Ceriani, Giulio Fontana, Alessandro Giusti, Daniele Marzorati,
60, (1950).
Matteo Matteucci, Davide Migliore, Davide Rizzi, Domenico G Sor[36] S. Ulbrich, D. Kappler, T. Asfour, N. Vahrenkamp, A. Bierbaum,
renti, and Pierluigi Taddei, ‘Rawseeds ground truth collection systems
M. Przybylski, and R. Dillmann, ‘The OpenGRASP Benchmarking
for indoor self-localization and mapping’, Autonomous Robots, 27(4),
Suite: An Environment for the Comparative Analysis of Grasping and
353–371, (2009).
Dexterous Manipulation’, in IEEE/RSJ International Conference on In[14] Enric Cervera, ‘Cross-Platform Software for Benchmarks on Visual
telligent Robots and Systems, (2011).
Servoing’, in IROS Workshop ong Benchmarks in Robotics Research,
[37] Kai Zhou, Michael Zillich, and Markus Vincze, ‘Mobile manipulation:
(2006).
Bring back the cereal box - Video proceedings of the 2011 CogX Spring
[15] R. Dillmann, ‘Benchmarks for Robotics Research’, Technical report,
School’, in 8th International Conference on Ubiquitous Robots and
EURON, (2004).
Ambient Intelligence (URAI), pp. 873–873. Automation and Control In[16] Malachy Eaton, J J Collins, and Lucia Sheehan, ‘Toward a benchmarkstitute, Vienna University of Technology, 1040, Austria, IEEE, (2011).
ing framework for research into bio-inspired hardware-software artefacts’, Artificial Life and Robotics, 5(1), 40–45, (2001).
[17] Brian P Gerkey, Richard T Vaughan, and Andrew Howard, ‘The Player /
Stage Project : Tools for Multi-Robot and Distributed Sensor Systems’,
in International Conference on Advanced Robotics (ICAR), pp. 317–
323, (2003).
[18] Gerhard Grunwald, Christoph Borst, and J. Marius Zöllner, ‘Benchmarking dexterous dual-arm/hand robotic manipulation’, in IROS
Workhop onPerformance Evaluation and Benchmarking for Intelligent
Robots and Systems, (2008).
[19] S Harnad, ‘Other Bodies, Other Minds: A Machine Incarnation of an
Old Philosophical Problem’, Minds and Machines, 1, 43–54, (1991).
[20] Zachary Henkel, Robin Murphy, Vasant Srinivasan, and Cindy Bethel,
‘A Proxemic-Based HRI Testbed’, in Proceedings of the Performance
Metrics for Intelligent Systems Workshop (PerMIS), (2012).
[21] I Iossifidis, G Lawitzky, S Knoop, and R Zöllner, ‘Towards Benchmarking of Domestic Robotic Assistants’, in Advances in Human Robot
Interaction, eds., Erwin Prassler, Gisbert Lawitzky, Andreas Stopp,
Gerhard Grunwald, Martin Hägele, Rüdiger Dillmann, and Ioannis
Iossifidis, volume 14/2004 of Springer Tracts in Advanced Robotics
{STAR}, chapter 7, 97–135, Springer Press, (2005).
[22] R. Jäkel, R., Schmidt-Rohr, S. R., Lösch, M., & Dillmann, ‘Hierarchical
structuring of manipulation benchmarks in service robotics’, in IROS
Workshop on Performance Evaluation and Benchmarking for Intelligent Robots and Systems with Cognitive and Autonomy Capabilities,
(2010).
[23] J.L. Jimenez, I. Rano, and I. Minguez, ‘Advances in the Framework
for Automatic Evaluation of Obstacle Avoidance Methods’, in IROS
Workshop on Benchmarks in Robotics Research, (2007).

ACKNOWLEDGEMENTS

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Interactive Intelligence: Behaviour-based AI,
Musical HCI and the Turing Test
Adam Linson, Chris Dobbyn and Robin Laney1
Abstract. The field of behaviour-based artificial intelligence (AI),
with its roots in the robotics research of Rodney Brooks, is not predominantly tied to linguistic interaction in the sense of the classic
Turing test (or, “imitation game”). Yet, it is worth noting, both are
centred on a behavioural model of intelligence. Similarly, there is
no intrinsic connection between musical AI and the language-based
Turing test, though there have been many attempts to forge connections between them. Nonetheless, there are aspects of musical AI
and the Turing test that can be considered in the context of nonlanguage-based interactive environments–in particular, when dealing
with real-time musical AI, especially interactive improvisation software. This paper draws out the threads of intentional agency and
human indistinguishability from Turing’s original 1950 characterisation of AI. On the basis of this distinction, it considers different
approaches to musical AI. In doing so, it highlights possibilities for
non-hierarchical interplay between human and computer agents.

1

Introduction

The field of behaviour-based artificial intelligence (AI), with its roots
in the robotics research of Rodney Brooks, is not predominantly tied
to linguistic interaction in the sense of the classic Turing test (or,
“imitation game” [24]). Yet, it is worth noting, both are centred on
a behavioural model of intelligence. Similarly, there is no intrinsic connection between musical AI and the language-based Turing
test, though there have been many attempts to forge connections between them. The primary approach to applying the Turing test to
music is in the guise of so-called “discrimination tests”, in which
human- and computer-generated musical output are compared (for
an extensive critical overview of how the Turing test has been applied to music, see [1]). Nonetheless, there are aspects of musical
AI and the Turing test that can be considered in the context of nonlanguage-based interactive environments—in particular, when dealing with real-time musical AI, especially interactive improvisation
software (see, for example, [23] and [8]). In this context, AI for nonhierarchical human-computer musical improvisation such as George
Lewis’ Voyager [16] and Turing’s imitation game are both examples
of “an open-ended and performative interplay between [human and
computer] agents that are not capable of dominating each other” [21].

2

Background

It is useful here to give some context to the Turing test itself. In its
original incarnation, the test was proposed as a thought experiment
to explain the concept of a thinking machine to a public uninitiated
1

Faculty of Mathematics, Computing and Technology, Dept. of Computing,
Open University, UK. Email: {a.linson, c.h.dobbyn, r.c.laney}@open.ac.uk

in such matters [24]. Rather than as a litmus test of whether or not
a machine could think (which is how the test is frequently understood), the test was in fact designed to help make sense of the concept of a machine that could think. Writing in 1950, he estimates
“about fifty years’ time” until the technology would be sufficient to
pass a real version of the test and states his belief “that at the end of
the century the use of words and general educated opinion will have
altered so much that one will be able to speak of machines thinking
without expecting to be contradicted”. Thus his original proposal remained a theoretical formulation: in principle, a machine could be
invented with the capacity to be mistaken for a human; if this goal
were accomplished, a reasonable person should accept the machine
as a thinking entity. He is very clear about the behaviourist underpinnings of the experiment:
May not machines carry out something which ought to be described as thinking but which is very different from what a man
does? This objection is a very strong one, but at least we can
say that if, nevertheless, a machine can be constructed to play
the imitation game satisfactorily, we need not be troubled by
this objection.
He goes on to describe the “imitation game” as one in which the
machine should “try to provide answers that would naturally be given
by a man”. His ideas became the basis for what eventually emerged
as the field of AI.
As Turing emphasised, the thought experiment consisted of an abstract, “imaginable” machine that—under certain conditions to ensure a level playing field—would be indistinguishable from a human, from the perspective of a human interrogator [24]. Presently,
when the test is actually deployed in practice, it is easy to forget the
essential role of the designer, especially given the fact that the computer “playing” the game is, to an extent, thrust into the spotlight. In a
manner of speaking, the interactive computer takes centre stage, and
attention is diverted from the underlying challenge set forth by Turing: to determine the specifications of the machine. Thus, one could
say in addition to being a test for a given machine, it is also a creative
design challenge to those responsible for the machine. The stress is
on design rather than implementation, as Turing explicitly suggests
imagining that any proposed machine functions perfectly according
to its specifications (see [24], p. 449). If the creative design challenge
were fulfilled, the computer would behave convincingly as a human,
perhaps hesitating when appropriate and occasionally refusing to answer or giving incorrect answers such as the ones Turing imagines
[24]:
Q: Please write me a sonnet on the subject of the Forth Bridge.
A: Count me out on this one. I never could write poetry.

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Q: Add 34957 to 70764.
A: (Pause about 30 seconds and then give as answer) 105621.
The implication of Turing’s example is that the measure of success
for those behind the machine lies in designing a system that is also
as stubborn and fallible as humans, rather than servile and (theoretically) infallible, like an adding machine.

3

Two threads unraveled

Two threads can be drawn out of Turing’s behavioural account of intelligence that directly pertain to contemporary AI systems: the first
one concerns the kind of intentional agency suggested by his example answer, “count me out on this one”; the second one concerns the
particular capacities and limitations of human embodiment, such as
the human inability to perform certain calculations in a fraction of a
second and the human potential for error. More generally, the second
thread has to do with the broadly construed linguistic, social, mental and physical consequences of human physiology. Indeed, current
theories of mind from a variety of disciplines provide a means for
considering these threads separately. In particular, relevant investigations that address these two threads—described in this context as
intentional agency and human indistinguishability—can be found in
psychology, philosophy and cognitive science.

3.1 Intentional agency
The first thread concerns the notion of intentional agency, considered here separately from the thread of human indistinguishability.
Empirical developmental psychology suggests that the human predisposition to attribute intentional agency to both humans and nonhumans appears to be present from infancy. Poulin-Dubois and Shultz
chart childhood developmental stages over the first three years of
life, from the initial ability to identify agency (distinguishing animate
from inanimate objects) on to the informed attribution of intentionality, by inference of goal-directed behavior [22]. Csibra found that
infants ascribed goal-directed behavior even to artificially animated
inanimate objects, if the objects were secretly manipulated to display
teleological actions such as obstacle avoidance [7]. Király, et al. identify the source of an infant’s interpretation of a teleological action: “if
the abstract cues of goal-directedness are present, even very young
infants are able to attribute goals to the actions of a wide range of
entities even if these are unfamiliar objects lacking human features”
[10].
It is important to note that in the above studies, the infants were
passive, remote observers, whereas the Turing test evaluates direct
interaction. While the predisposition of infants suggests an important basis for such evaluation, more is needed to address interactivity. In another area of empirical psychology, a study of adults by
Barrett and Johnson suggests that even a lack of apparent goals by a
self-propelled (nonhuman) object can lead to the attribution of intentionality in an interactive context [2]. In particular, their test subjects
used language normally reserved for humans and animals to describe
the behaviour of artificially animated inanimate objects that appeared
to exhibit resistance to direct control in the course of an interaction;
when there was no resistance, they did not use such language. The
authors of the study link the results of their controlled experiment to
the anecdotal experience of the frustration that arises during interactions with artifacts such as computers or vehicles that “refuse” to
cooperate. In other words, in an interactive context, too much passivity by an artificial agent may negate any sense of its apparent

intentionality. This suggests that for an agent to remain apparently
intentional during direct interaction, it must exhibit a degree of resistance along with the kind of adaptation to the environment that indicates its behaviour is being adjusted to attain a goal. These features
appear to be accounted for in Turing’s first example answer above:
the answer is accommodating insofar as it is a direct response to the
interrogator, but the show of resistance seems to enhance the sense
of “intelligence”. It is noteworthy that this particular thread, intentional agency, relates closely to Brooks’ extension of intelligence to
nonlinguistic, nonhuman intelligence, especially in relation to insect
and other animal intelligence, which he has emulated in robotic form
with his particular approach to AI (see [3]).

3.2 Human indistinguishability
The second thread, the idea that human capacities and limitations
should be built into an AI system, strongly relates to many significant accounts of embodied, situated activity (see, for example, [9],
[4] and [11]). These accounts focus on how the human body, brain,
mind and environment fundamentally structure the process of cognition, which can be understood through observable behaviour. When
dealing with AI, the focus on behaviour clearly ties back to Turing.
These themes are also taken up in Brooks’ behaviour-based AI approach, but, at least in his early research, he applies them primarily
to nonhuman intelligence. In particular, he relates these themes to the
kinds of adaptive behaviour described in the first thread. The differing properties of the second thread will come into sharper focus by
returning to Turing’s example, for a consideration of matters particular to humans.
Although Turing’s example of pausing and giving an incorrect answer is a clear example of a human limitation over a machine, it is
possible to give an inverted example of human and machine competence that applies equally well. If the question posed to the machine
were instead “Is it easy to walk from here to the nearest supermarket?”, the machine’s answer would depend on how its designers handled the notion of “easy to walk to”. In this case, the machine must
not only emulate humans’ abstract cognitive limitations when solving arithmetical problems; it must also be able to respond according
to human bodily limitations. One could easily imagine a failed machine calculation: the supermarket is at the end of a single straight
road, with no turns; it answers “yes, it is easy to walk to”. But if the
supermarket is very distant, or nearby but up a steep incline, then
in order for the machine to give an answer that is indistinguishable
from a human one, it must respond in a way that seems to share
our embodied human limitations. Returning to the arithmetic example, as Doug Lenat points out, even some wrong answers are more
human than others: “93 − 25 = 78 is more understandable than
if the program pretends to get a wrong answer of 0 or −9998 for
that subtraction problem” [14]. Although Lenat disputes the need for
embodiment in AI (he prefers a central database of human common
sense [13], which could likely address the “easy to walk to” example), it could be argued, following the above theoretical positions,
that the set of humanlike wrong answers is ultimately determined by
the “commonalities of our bodies and our bodily and social experience in the world” [11].
This second thread, which could also be characterised as the attempt to seem humanlike, is taken up in another nonlinguistic area of
AI, namely, musical AI. Some “intelligent” computer music composition and performance systems appear very close to achieving human indistinguishability in some respects, although this is not always their explicitly stated purpose. For example, Manfred Clynes

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17

describes a computer program that performs compositions by applying a single performer’s manner of interpretation to previously unencountered material, across all instrumental voices [5]. He states
that “our computer program plays music so that it is impossible to
believe that no human performer is involved,” which he qualifies by
explaining the role of the human performer as a user of the software,
who “instills the [musical performance] principles in the appropriate way”. Taking an entirely different approach, David Cope, argues
that a Turing-like test for creativity would be more appropriate to his
work than a Turing test for intelligence [6]. On the other hand, he has
called his well-known project “Experiments in Musical Intelligence”
and he also makes reference to “intelligent music composition”. Furthermore, he states that his system generates “convincing” music in
the style of a given composer (by training the system with a corpus of human-composed music), and one can infer that, in this context, “convincing” at least approximates the notion of human indistinguishability. With a more critical articulation, Pearce and Wiggins
carefully differentiate between a test for what Cope calls “convincing” and a Turing test for intelligence [19]. As they point out, despite
the resemblance of the two approaches, testing for intelligence is distinct from determining the “(non-)membership of a machine composition in a set of human composed pieces of music”. They also note
the significant difference between an interactive test and one involving passive observation.

4

Broadening the interactive horizon

One reason for isolating these two threads is to recast Turing’s ideas
in a wider social context, one that is better attuned to the contemporary social understanding of the role of technology research: namely,
that it is primarily intended (or even expected) to enhance our lives.
Outside the thought experiment, in the realm of practical application,
one might redirect the resources for developing a successful Turing
test candidate (e.g., for the Loebner Prize) and instead apply them
toward a different kind of interactive system. This proposed system
could be built so that it might be easily identified as a machine (even
if occasionally mistaken for a human), which seemingly runs counter
to the spirit of the Turing test. However, with an altered emphasis,
one could imagine the primary function of such a machine as engaging humans in a continuous process of interaction, for a variety of
purposes, including (but not limited to) stimulating human creativity
and providing a realm for aesthetic exploration.
One example of this kind of system is musical improvisation software that interacts with human performers in real time, in a mutually
influential relationship between human and computer, such as Lewis’
Voyager. In his software design, the interaction model strongly resembles the way in which Turing describes a computer’s behaviour:
it is responsive, yet it does not always give the expected answer, and
it might interrupt the human interlocutor or steer the interaction in a
different direction (see [16]). In the case of an interactive improvising
music system, the environment in which the human and computer interact is not verbal conversation, but rather, a culturally specific aesthetic context for collaborative music-making. In this sense, a musical improvisation is not an interrogation in the manner presented by
Turing, yet “test” conversations and musical improvisations are examples of free-ranging and open-ended human-computer interaction.
Among other things, this kind of interaction can serve as a basis for
philosophical enquiry and cognitive theory that is indeed very much
in the spirit of Turing’s 1950 paper [24] (see also [15] and [17]).
Adam Linson’s Odessa is another intelligent musical system that
is similarly rooted in freely improvised music (for a detailed descrip-

tion, see [18]). It borrows from Brooks’ design approach in modelling the behaviour of an intentional agent, thus clearly taking up
the first thread that has been drawn out here. Significantly, it isolates this thread (intentional agency) for study by abstaining from
a direct implementation of many of the available methods for human emulation (aimed at the second thread), thus resulting in transparently nonhuman musical behaviour. Nonetheless, initial empirical studies suggest that the system affords an engaging and stimulating human-computer musical interaction. As the system architecture
(based on Brooks’ subsumption architecture) is highly extensible, future iterations of the system may add techniques for approximating
fine-grained qualities of human musicianship. In the meantime, however, further studies are planned with the existing prototype, with the
aim of providing insights into aspects of human cognition as well as
intelligent musical agent design.

5

Conclusion

Ultimately, whether an interactive computer system is dealing with
an interrogator in the imitation game or musically improvising with
a human, the system must be designed to “respond in lived real
time to unexpected, real-world input” [17]. This responsiveness takes
the form of what sociologist Andrew Pickering calls the “dance of
agency”, in which a reciprocal interplay of resistance and accommodation produces unpredictable emergent results over time [20].
This description of a sustained, continuous play of forces that “interactively stablize” each other could be applied to freely improvised
music, whether performed by humans exclusively, or by humans and
computers together. Pickering points out a concept similar to the process of interactive stabilisation, ‘heterogeneous engineering’, elaborated in the work of his colleague John Law (see [12]); the latter, in
its emphasis on productive output, is perhaps more appropriate to the
musical context of free improvisation.
Although these theoretical characterisations may seem abstract,
they concretely pertain to the present topic in that they seek to address the “open-ended and performative interplay between agents
that are not capable of dominating each other” [21], where the agents
may include various combinations of humans, computers and other
entities, and the interplay may include linguistic, musical, physical and other forms of interaction. With particular relevance to the
present context, Pickering applies his conceptual framework of agent
interplay to the animal-like robots of Turing’s contemporary, cybernetics pioneer Grey Walter, and those of Brooks, designed and built
decades later [21]. Returning to the main theme, following Brooks,
“the dynamics of the interaction of the robot and its environment are
primary determinants of the structure of its intelligence” [3]. Thus,
independent of its human resemblance, an agent’s ability to negotiate
with an unstructured and highly dynamic musical, social or physical environment can be treated as a measure of intelligence closely
aligned with what Turing thought to be discoverable with his proposed test.

REFERENCES
[1] C. Ariza, ‘The interrogator as critic: The turing test and the evaluation
of generative music systems’, Computer Music Journal, 33(2), 48–70,
(2009).
[2] J.L. Barrett and A.H. Johnson, ‘The role of control in attributing intentional agency to inanimate objects’, Journal of Cognition and Culture,
3(3), 208–217, (2003).
[3] R.A. Brooks, Cambrian intelligence: the early history of the new AI,
MIT Press, 1999.

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[4] A. Clark, Being There: Putting Brain, Body, and World Together Again,
MIT Press, 1997.
[5] M. Clynes, ‘Generative principles of musical thought: Integration of
microstructure with structure’, Communication and Cognition AI, Journal for the Integrated Study of Artificial Intelligence, Cognitive Science
and Applied Epistemology, 3(3), 185–223, (1986).
[6] D. Cope, Computer Models of Musical Creativity, MIT Press, 2005.
[7] G. Csibra, ‘Goal attribution to inanimate agents by 6.5-month-old infants’, Cognition, 107(2), 705–717, (2008).
[8] R.T. Dean, Hyperimprovisation: Computer-interactive sound improvisation, AR Editions, Inc., 2003.
[9] H. Hendriks-Jansen, Catching ourselves in the act: Situated activity,
interactive emergence, evolution, and human thought, MIT Press, 1996.
[10] I. Király, B. Jovanovic, W. Prinz, G. Aschersleben, and G. Gergely,
‘The early origins of goal attribution in infancy’, Consciousness and
Cognition, 12(4), 752–769, (2003).
[11] G. Lakoff and M. Johnson, Philosophy in the Flesh: The Embodied
Mind and Its Challenge to Western Thought, Basic Books, 1999.
[12] J. Law, ‘On the social explanation of technical change: The case of the
portuguese maritime expansion’, Technology and Culture, 28(2), 227–
252, (1987).
[13] D.B. Lenat, ‘Cyc: A large-scale investment in knowledge infrastructure’, Communications of the ACM, 38(11), 33–38, (1995).
[14] D.B. Lenat, ‘The voice of the turtle: Whatever happened to ai?’, AI
Magazine, 29(2), 11, (2008).
[15] G. Lewis, ‘Interacting with latter-day musical automata’, Contemporary Music Review, 18(3), 99–112, (1999).
[16] G. Lewis, ‘Too many notes: Computers, complexity and culture in voyager’, Leonardo Music Journal, 33–39, (2000).
[17] G. Lewis, ‘Improvising tomorrow’s bodies: The politics of transduction’, E-misférica, 4.2, (2007).
[18] A. Linson, C. Dobbyn, and R. Laney, ‘Improvisation without representation: artificial intelligence and music’, in Proceedings of Music, Mind,
and Invention: Creativity at the Intersection of Music and Computation,
(2012).
[19] M. Pearce and G. Wiggins, ‘Towards a framework for the evaluation of
machine compositions’, in Proceedings of the AISB, pp. 22–32, (2001).
[20] A. Pickering, The mangle of practice: Time, agency, and science, University of Chicago Press, 1995.
[21] A. Pickering, The cybernetic brain: Sketches of another future, University of Chicago Press, 2010.
[22] D. Poulin-Dubois and T.R. Shultz, ‘The development of the understanding of human behavior: From agency to intentionality’, in Developing
Theories of Mind, eds., Janet W. Astington, Paul L. Harris, and David R.
Olson, 109–125, Cambridge University Press, (1988).
[23] R. Rowe, Machine musicianship, MIT Press, 2001.
[24] A.M. Turing, ‘Computing machinery and intelligence’, Mind, 59(236),
433–460, (1950).

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

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The AN Y NT Project Intelligence Test Λone
Javier Insa-Cabrera1 and José Hernández-Orallo2 and David L. Dowe3
and Sergio España4 and M.Victoria Hernández-Lloreda5
Abstract. All tests in psychometrics, comparative psychology and
cognition which have been put into practice lack a mathematical
(computational) foundation or lack the capability to be applied to
any kind of system (humans, non-human animals, machines, hybrids,
collectives, etc.). In fact, most of them lack both things. In the past
fifteen years, some efforts have been done to derive intelligence tests
from formal intelligence definitions or vice versa, grounded on computational concepts. However, some of these approaches have not
been able to create universal tests (i.e., tests which can evaluate any
kind of subjects) and others have even failed to make a feasible test.
The AN Y NT project was conceived to explore the possibility of defining formal, universal and anytime intelligence tests, having a feasible
implementation in mind. This paper presents the basics of the theory
behind the AN Y NT project and describes one of the test propotypes
that were developed in the project: test Λone .
Keywords: (machine) intelligence evaluation, universal tests, artificial intelligence, Solomonoff-Kolmogorov complexity.

1

INTRODUCTION

There are many examples of intelligence tests which work in practice. For instance, in psychometrics and comparative psychology,
tests are used to evaluate intelligence for a variety of subjects: children and adult Homo Sapiens, other apes, cetaceans, etc. In artificial intelligence, we are well aware of some incarnations and different variations of the Turing Test, such as the Loebner Prize or
CAPTCHAs [32], which are also feasible and informative. However,
they do not answer the pristine questions: what intelligence is and
how it can be built.
In the past fifteen years, some efforts have been done to derive
intelligence tests from formal intelligence definitions or vice versa,
grounded on computational concepts. However, some of these approaches have not been able to create universal tests (i.e., tests which
can evaluate any kind of subjects) and others have even failed to
make a feasible test. The AN Y NT project6 was conceived to explore
the possibility of defining formal, universal and anytime intelligence
tests, having a feasible implementation in mind.
1
2
3
4
5
6

DSIC, Universitat Politècnica de València, Spain. email:
jinsa@dsic.upv.es
DSIC, Universitat Politècnica de València, Spain. email:
jorallo@dsic.upv.es
Clayton School of Information Technology, Monash University, Australia.
email: david.dowe@monash.edu
PROS, Universitat Politècnica de València, Spain. email:
sergio.espana@pros.upv.es
Universidad
Complutense
de
Madrid,
Spain.
email:
vhlloreda@psi.ucm.es
http://users.dsic.upv.es/proy/anynt/

In the AN Y NT project we have been working on the design and
implementation of a general intelligence test, which can be feasibly
applied to a wide range of subjects. More precisely, the goal of the
project is to develop intelligence tests that are: (1) formal, by using
notions from Algorithmic Information Theory (a.k.a. Kolmogorov
Complexity) [24]; (2) universal, so that they are able to evaluate the
general intelligence of any kind of system (human, non-human animal, machine or hybrid). Each will have an appropriate interface that
fits its needs; (3) anytime, so the more time is available for the evaluation, the more reliable the measurement will be.

2

BACKGROUND

In this section, we present a short introduction to the area of Algorithmic Information Theory and the notions of Kolmogorov complexity,
universal distributions, Levin’s Kt complexity, and its relation to the
notions of compression, the Minimum Message Length (MML) principle, prediction, and inductive inference. Then, we will survey the
approaches that have appeared using these formal notions in order
to give mathematical definitions of intelligence or to develop intelligence tests from them, starting from the compression-enhanced Turing tests, the C-test, and Legg and Hutter’s definition of Universal
Intelligence.

2.1 Kolmogorov complexity and universal
distributions
Algorithmic Information Theory is a field in computer science that
properly relates the notions of computation and information. The key
idea is the notion of the Kolmogorov Complexity of an object, which
is defined as the length of the shortest program p that outputs a given
string x over a machine U . Formally,
Definition 1 Kolmogorov Complexity
KU (x) :=
p

min
l(p)
such that U (p)=x

where l(p) denotes the length in bits of p and U (p) denotes the result
of executing p on U .
For instance, if x = 1010101010101010 and U is the programming language Lisp, then KLisp (x) is the length in bits of the shortest program in Lisp that outputs the string x. The relevance of the
choice of U depends mostly on the size of x. Since any universal
machine can emulate another, it holds that for every two universal
Turing machines U and V , there is a constant c(U, V ), which only
depends on U and V and does not depend on x, such that for all x,
|KU (x) − KV (x)| ≤ c(U, V ). The value of c(U, V ) is relatively
small for sufficiently long x.

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20

From Definition 1, we can define the universal probability for machine U as follows:
Definition 2 Universal Distribution
Given a prefix-free machine7 U , the universal probability of string
x is defined as:
pU (x) := 2−KU (x)

which gives higher probability to objects whose shortest description
is small and gives lower probability to objects whose shortest description is large. Considering programs as hypotheses in the hypothesis language defined by the machine, paves the way for the mathematical theory of inductive inference and prediction. This theory
was developed by Solomonoff [28], formalising Occam’s razor in a
proper way for prediction, by stating that the prediction maximising
the universal probability will eventually discover any regularity in the
data. This is related to the notion of Minimum Message Length for
inductive inference [34][35][1][33] and is also related to the notion
of data compression.
One of the main problems of Algorithmic Information Theory is
that Kolmogorov Complexity is uncomputable. One popular solution to the problem of computability of K for finite strings is to use a
time-bounded or weighted version of Kolmogorov complexity (and,
hence, the universal distribution which is derived from it). One popular choice is Levin’s Kt complexity [23][24]:
Definition 3 Levin’s Kt Complexity
KtU (x) :=
p

min
{l(p) + log time(U, p, x)}
such that U (p)=x

where l(p) denotes the length in bits of p, U (p) denotes the result of
executing p on U , and time(U, p, x) denotes the time8 that U takes
executing p to produce x.
Finally, despite the uncomputability of K and the computational
complexity of its approximations, there have been some efforts to use
Algorithmic Information Theory to devise optimal search or learning
strategies. Levin (or universal) search [23] is an iterative search algorithm for solving inversion problems based on Kt, which has inspired other general agent policies such as Hutter’s AIXI, an agent
that is able to adapt optimally9 in all environments where any other
general purpose agent can be optimal [17], for which there is a working approximation [31][30].

2.2 Developing mathematical definitions and tests
of intelligence
Following ideas from A.M. Turing, R.J. Solomonoff, E.M. Gold,
C.S. Wallace, M. Blum, G. Chaitin and others, between 1997 and
7

For a convenient definition of the universal probability, we need the requirement of U being a prefix-free machine (see, e.g., [24] for details). Note also
that even for prefix-free machines there are infinitely many other inputs to
U that will output x, so pU (x) is a strict lower bound on the probability
that U will output x (given a random input)
8 Here time does not refer to physical time but to computational time, i.e.,
computation steps taken by machine U . This is important, since the complexity of an object cannot depend on the speed of the machine where it is
run.
9 Optimality has to be understood in an asymptotic way. First, because AIXI
is uncomputable (although resource-bounded variants have been introduced
and shown to be optimal in terms of time and space costs). Second, because
it is based on a universal probability over a machine, and this choice determines a constant term which may very important for small environments.

1998 some works on enhancing or substituting the Turing Test [29]
by inductive inference tests were developed, using Solomonoff prediction theory [28] and related notions, such as the Minimum Message Length (MML) principle. On the one hand, Dowe and Hajek
[2][3][4] suggested the introduction of inductive inference problems
in a somehow induction-enhanced or compression-enhanced Turing
Test (they arguably called it non-behavioural) in order to, among
other things, completely dismiss Searle’s Chinese room [27] objection, and also because an inductive inference ability is a necessary
(though possibly “not sufficient”) requirement for intelligence.
Quite simultaneously and similarly, and also independently, in
[13][6], intelligence was defined as the ability to comprehend, giving a formal definition of the notion of comprehension as the identification of a ‘predominant’ pattern from a given evidence, derived
from Solomonoff prediction theory concepts, Kolmogorov complexity and Levin’s Kt. The notion of comprehension was formalised by
using the notion of “projectible” pattern, a pattern that has no exceptions (no noise), so being able to explain every symbol in the given
sequence (and not only most of it).
From these definitions, the basic idea was to construct a feasible
test as a set of series whose shortest pattern had no alternative projectible patterns of similar complexity. That means that the “explanation” of the series had to be much more plausible than other plausible
hypotheses. The main objective was to reduce the subjectivity of the
test — first, because we need to choose one reference universal machine from an infinite set of possibilities; secondly, because, even
choosing one reference machine, two very different patterns could
be consistent with the evidence and if both have similar complexities,
their probabilities would be close, and choosing between them would
make the series solution quite uncertain. With the constraints posed
on patterns and series, both problems were not completely solved but
minimised.
k=9
k = 12
k = 14

:
:
:

a, d, g, j, ...
a, a, z, c, y, e, x, ...
c, a, b, d, b, c, c, e, c, d, ...

Answer: m
Answer: g
Answer: d

Figure 1. Examples of series of Kt complexity 9, 12, and 14 used in the
C-test [6].

The definition was given as the result of a test, called C-test [13],
formed by computationally-obtained series of increasing complexity.
The sequences were formatted and presented in a quite similar way
to psychometric tests (see Figure 1) and, as a result, the test was administered to humans, showing a high correlation with the results of
a classical psychometric (IQ) test on the same individuals. Nonetheless, the main goal was that the test could eventually be administered
to other kinds of intelligent beings and systems. This was planned
to be done, but the work from [26] showed that machine learning
programs could be specialised in such a way that they could score
reasonably well on some of the typical IQ tests. A more extensive
treatment of this phenomenon and the inadequacy of current IQ tests
for evaluating machines can be found in [5]. This unexpected result
confirmed that C-tests had important limitations and could not be
considered universal in two ways, i.e., embracing the whole notion
of intelligence, but perhaps only a part of it, and being applicable to
any kind of subject (not only adult humans). The idea of extending
these static tests to other factors or to make them interactive and extensible to other kinds of subjects by the use of rewards (as in the
area of reinforcement learning) was suggested in [7][8], but not fully

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21

developed into actual tests. An illustration of the classical view of an
environment in reinforcement learning is seen in Figure 2, where an
agent can interact through actions, rewards and observations.
!"#$%&'()!*

!"#$%

%$+'%,

#$&'()$*#$%

'-()!*

Figure 2. Interaction with an Environment.

A few years later, Legg and Hutter (e.g. [21],[22]) followed the
previous steps and, strongly influenced by Hutter’s theory of AIXI
optimal agents [16], gave a new definition of machine intelligence,
dubbed “Universal10 Intelligence”, also grounded in Kolmogorov
complexity and Solomonoff’s (“inductive inference” or) prediction
theory. The key idea is that the intelligence of an agent is evaluated
as some kind of sum (or weighted average) of performances in all the
possible environments (as in Figure 2).
The definition based on the C-test can now be considered a static
precursor of Legg and Hutter’s work, where the environment outputs no rewards, and the agent is not allowed to make an action until
several observations are seen (the inductive inference or prediction
sequence). The point in favour of active environments (in contrast
to passive environments) is that the former not only require inductive and predictive abilities to model the environment but also some
planning abilities to effectively use this knowledge through actions.
Additionally, perceptions, selective attention, and memory abilities
must be fully developed. Not all this is needed to score well in a
C-test, for instance.
While the C-test selects the problems by (intrinsic) difficulty
(which can be chosen to fit the level of intelligence of the evaluee),
Legg and Hutter’s approach select problems by using a universal distribution, which gives more probability to simple environments. Legg
and Hutter’s definition, given an agent π, is given as:
Definition 4 Universal Intelligence [22]
"∞
#
∞
! µ,π
!
ri
Υ(π, U ) =
pU (µ) · E
µ=i

i=1

where µ is any environment coded on a universal machine U , with π
being the agent to be evaluated, and riµ,π the reward obtained by π
in µ at interaction i. E is the expected reward on each environment,
where environments are assigned with probability pU (µ) using a universal distribution [28].
Definition 4, although very simple, captures one of the broadest
definitions of intelligence: “the ability to adapt to a wide range of environments”. However, this definition was not meant to be eventually
converted into a test. In fact, there are three obvious problems in this
definition regarding making it practical. First, we have two infinite
sums in the definition: one is the sum over all environments, and the
10

The term ‘universal’ here does not refer to the definition (or a derived
test) being applicable to any kind of agent, but to the use of Solomonoff’s
universal distribution and the view of the definition as an extremely general
view of intelligence.

second is the sum over all possible actions (agent’s life in each environment is infinite). And, finally, K is not computable. Additionally,
we also have the dependence on the reference machine U . This dependence takes place even though we consider an infinite number of
environments. The universal distribution for a machine U could give
the higher probabilities (0.5, 0.25, ...) to quite different environments
than those given by another machine V .
Despite all these problems, it could seem that just making a random finite sample on environments, limiting the number of interactions or cycles of the agent with respect to the environment and using
some computable variant of K, is sufficient to make it a practical test.
However, on the one hand, this is not so easy, and, on the other hand,
the definition has many other problems (some related and others unrelated).
The realisation of these problems and the search for solutions in
the quest of a practical intelligence test is the goal of the AN Y NT
project.

3

ANYTIME UNIVERSAL TESTS

This section presents a summary of the theory in [11]. The reader is
referred to this paper for further details.

3.1 On the difficulty of environments
The first issue concerns how to sample environments. Just using the
universal distribution for this , as suggested by Legg and Hutter, will
mean that very simple environments will be output again and again.
Note that an environment µ with K(µ) = 1 will appear half of the
time. Of course, repeated environments must be ruled out, but a sample would almost become an enumeration from low to high K. This
will still omit or underweight very complex environments because
their probability is so low. Furthermore, measuring rewards on very
small environments will get very unstable results and be very dependent on the reference machine. And even ignoring this, it is not clear
that an agent that solves all the problems of complexity lower than
20 bits and none of those whose complexity is larger than 20 bits
is more intelligent than another agent who does reasonably well on
every environment.
This constrasts with the view of the C-test, which focus on the
issue of difficulty and does not make the probability of a problem appearing inversely related to this difficulty. In any case, before
going on, we need to clarify the notions of simple/easy and complex/difficult that are used here. For instance, just choosing an environment with high K does not ensure that the environment is indeed
complex. As Figure 3 illustrates, the relation is unidirectional; given
a low K, we can affirm that the environment will look simple. On
the other hand, given an intuitively complex environment, K must
be necessarily high.
Environment with high K ⇐= Intuitively complex (difficult) environment
Environment with low K =⇒ Intuitively simple (easy) environment

Figure 3. Relation between K and intuitive complexity.

Given this relation, only among environments with high K will
we find complex environments, and, among the latter, not all of them
will be difficult. From the agent’s perspective, however, this is more
extreme, since many environments with high K will contain difficult patterns that will never be accessed by the agent’s interactions.

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22

As a result, the environment will be probabilistically simple. Thus,
giving most of the probability to environments with low K means
that most of the intelligence measure will come from patterns that
are extremely simple.

3.2 Selecting discriminative environments
Furthermore, many environments (either simple or complex) will
be completely useless for evaluating intelligence, e.g., environments
that stop interacting, environments with constant rewards, etc. If
we are able to make a more accurate sample, we will be able to
make a more efficient test procedure. The question here is to determine a non-arbitrary criterion to exclude some environments. For instance, Legg and Hutter’s definition forces environments to interact
infinitely, and since the description must be finite, there must be a pattern. This obviously includes environments such as “always output
the same observation and reward”. In fact, they are not only possible
but highly probable on many reference machines. Another pathological case is an environment that “outputs observations and rewards
at random”. However, this has a high complexity if we assume deterministic environments. In both cases, the behaviour of any agent
on these environments would almost be the same. In other words,
they do not have discriminative power. Therefore, these environments would be useless for discriminating between agents.
In an interactive environment, a clear requirement for an environment to be discriminative is that what the agent does must have consequences on rewards. Thus, we will restrict environments to be sensitive to agents’ actions. That means that a wrong action might lead
the agent to part of the environment from which it can never return
(non-ergodic), but at least the actions taken by the agent can modify the rewards in that subenvironment. More precisely, we want an
agent to be able to influence rewards at any point in any subenvironment. This does not imply ergodicity but reward sensitivity at any
moment. That means that we cannot reach a point from which rewards are given independently of what we do (a dead-end).

3.3 Symmetric rewards and balanced
environments
An important issue is how to estimate rewards. If we only use positive
rewards, we find some problems. For example, an increase in the
score may originate from a really good behaviour on the environment
or just because more rewards are accumulated since they are always
positive. Instead, an average reward seems a better payoff function.
Our proposal is to use symmetric rewards, which can range between
−1 and 1:
Definition 5 Symmetric Rewards
We say an environment has symmetric rewards when:
∀i : −1 ≤ ri ≤ 1
If we set symmetric rewards, we also expect environments to be
symmetric, or more precisely, to be balanced on how they give rewards. This can be seen in the following way. In a reliable test, we
would like that many (if not all) environments give an expected 0
reward to random agents.
This excludes both hostile and benevolent environments, i.e., environments where doing randomly will get more negative (respectively positive) rewards than positive (respectively negative) rewards.
In many cases it is not difficult to prove that a particular environment

is balanced. Another approach is to set a reference machine that only
generates balanced environments.
Using this approach on rewards, we can use an average to estimate
the results on each environment, namely:
Definition 6 Average Reward
Given an environment µ, with ni being the number of completed
interactions, then the average reward for agent π is defined as follows:
!ni µ,π
i=1 ri
vµπ (ni ) =
ni
Now we can calculate the expected value (although the limit may
not exist) of the previous average, denoted by E(vµπ ), for an arbitrarily large value of ni .
To view the test framework in more detail, in [11] some of these
issues (and many other problems) of the measure are solved. It uses
a random finite sample of environments. It limits the number of interactions of the agent with respect to the environment. It selects a
discriminative set of environments, etc.

4

ENVIRONMENT CLASS

The previous theory, however, does not make the choice for an environment class, but just sets some constraints on the kind of environments that can be used. Consequently, one major open problem is to
make this choice, i.e., to find a proper (unbiased) environment class
which follows the constraints and, more difficult, which can be feasibly implemented. Once this environment class is identified, we can
use it to generate environments to run any of the tests variants. Additionally, it is not only necessary to determine the environment class,
but also to determine the universal machine we will use to determine
the Kolmogorov complexity of each environment, since the tests only
use a (small) sample of environments, and the sample probability is
defined in terms of the complexity.
In the previous section we defined a set of properties which are
required for making environments discriminative, namely that observations and rewards must be sensitive to agent’s actions and that
environments are balanced. Given these constraints if we decide to
generate environments without any constraint and then try to make
a post-processing sieve to select which of them comply with all the
constraints, we will have a computationally very expensive or even
incomputable problem. So, the approach taken is to generate an environment class that ensures that these properties hold. In any case,
we have to be very careful, because we would not like to restrict
the reference machine to comply with these properties at the cost of
losing their universality (i.e. their ability to emulate or include any
computable function).
And finally, we would like the environment class to be userfriendly to the kind of systems we want to be evaluated (humans,
non-human animals and machines), but without any bias in favour or
against some of them.
According to all this, we define a universal environment class from
which we can effectively generate valid environments, calculate their
complexity and consequently derive their probability.

4.1 On actions, observations and space
Back to Figure 2 again, actions are limited by a finite set of symbols
A, (e.g. {lef t, right, up, down}), rewards are taken from any subset
R of rational numbers between −1 and 1, and observations are also

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23

limited by a finite set O of possibilities (e.g., the contents of a grid
of binary cells of n × m, or a set of light-emitting diodes, LEDs).
We will use ai , ri and oi to (respectively) denote action, reward and
observation at interaction i.
Apart from the behaviour of an environment, which may vary from
very simple to very complex, we must first clarify the interface. How
many actions are we going to allow? How many different observations? The very definition of environment makes actions a finite set
of symbols and observations are also a finite set of symbols. It is clear
that the minimum number of actions has to be two, but no upper limit
seems to be decided a priori. The same happens with observations.
Even choosing two for both, a sequence of interactions can be as rich
as the expressiveness of a Turing machine.
Before getting into details with the interface, we have to think
about environments that can contain agents. This is not only the case
in real life (where agents are known as inanimate or animate objects,
animals among the latter), but also a requirement for evolution and,
hence, intelligence as we know it. The existence of several agents
which can interact requires a space. The space is not necessarily a
virtual or physical space, but also a set of common rules (or laws)
that govern what the agents can perceive and what the agents can do.
From this set of common rules, specific rules can be added to each
agent. In the real world, this set of common rules is physics. All this
has been extensively analysed in multi-agent systems (see e.g. [20]
for a discussion).
The good thing about thinking of spaces is that a space entails the
possible perceptions and actions. If we define a common space, we
have many choices about observations and actions already taken.
A first (and common) idea for a space is a 2D grid. From a 2D grid,
the observation is a picture of the grid with all the objects and agents
inside. In a simple grid where we have agents and objects inside the
cells, the typical actions are the movements left, right, up and down.
Alternatively, of course, we could use a 3D space, since our world
is 3D. In fact, there are some results using intelligence testing (for
animals or humans) with a 3D interface [25][36].
The problem of a 2D or 3D grid is that it is clearly biased in favour
of humans and many other animals which have hardwired abilities
for orientation in this kind of spaces. Other kinds of animals or handicapped people (e.g. blind people) might have some difficulties in
this type of spaces. Additionally, artificial intelligence agents would
highly benefit by hardwired functionalities about Euclidean distance
and 2D movement, without any real improvement in their general
intelligence.
Instead we propose a more general kind of space. A 2D grid is a
graph with a very special topology, where there are concepts which
hold such as direction, adjacency, etc. A generalisation is a graph
where the cells are freely connected to some other cells with no particular predefined pattern. This suggests a (generally) dimensionless
space. Connections between cells would determine part or all the
possible actions, and observations and rewards can be easily shown
graphically.

4.2 Definition of the environment class
After the previous discussion, we are ready to give the definition of
the environment class. First we must define the space and objects, and
from here observations, actions and rewards. Before that, we have to
define some constants that affect each environment. Namely, with
na = |A| ≥ 2 we denote the number of actions, with nc ≥ 2
the number of cells, and with nω the number of objects/agents (not
including the agent which is to be evaluated and two special objects

known as Good and Evil).

4.2.1

Space

The space is defined as a directed labelled graph of nc nodes (or
vertices), where each node represents a cell. Nodes are numbered,
starting from 1, so cells are refered to as C1 , C2 , . . . , Cnc . From each
cell we have na outgoing arrows (or arcs), each of them denoted as
Ci →α Cj , meaning that action α ∈ A goes from Ci to Cj . All the
"i . At least two outgoing
outgoing arrows from Ci are denoted by C
"i such
arrows cannot go to the same cell. Formally, ∀Ci : ∃r1 , r2 ∈ C
that r1 = Ci →αm Cj and r2 = Ci →αn Ck with Cj '= Ck and
αm '= αn . At least one of the outgoing arrows from a cell must lead
to itself (typically denoted by α1 and is the first action). Formally,
"i such that r = Ci →α1 Ci .
∀Ci : ∃r ∈ C
A path from Ci to Cm is a sequence of arrows Ci → Cj , Cj →
Ck , . . . , Cl → Cm . The graph must be strongly connected, i.e., all
cells must be connected (i.e. there must be a walk over the graph that
goes through all its nodes), or, in other words, for every two cells Ci ,
Cj there exists a path from Ci to Cj .

4.2.2

Objects

Cells can contain objects from a set of predefined objects Ω, with
nω = |Ω|. Objects, denoted by ωi can be animate or inanimate, but
this can only be perceived by the rules each object has. An object is
inanimate (for a period or indefinitely) when it performs action α1
repeatedly. Objects can perform actions following the space rules,
but apart from these rules, they can have any behaviour, either deterministic or not. Objects can be reactive and can be defined to act
with different actions according to their observations. Objects perform one and only one action at each interaction of the environment
(except from the special objects Good and Evil, which can perform
several actions in a row).
Apart from the evaluated agent π, as we have mentioned, there
are two special objects called Good and Evil. Good and Evil must
have the same behaviour. By the same behavior we do not mean that
they perform the same movements, but they have the same logic or
program behind them.
Objects can share a same cell, except Good and Evil, which cannot
be at the same cell. If their behaviour leads them to the same cell, then
one (chosen randomly with equal probability) moves to the intended
cell and the other remains at its original cell. Because of this, the
environment becomes stochastic (non-deterministic).
Objects are placed randomly at the cells with the initialisation of
the environment. This is another source of stochastic behaviour.

4.2.3

Observations and Actions

The observation is a sequence of cell contents. The cells are ordered
by their number. Each element in the sequence shows the presence
or absence of each object, included the evaluated agent. Additionally,
each cell which is reachable by an action includes the information of
that action leading to the cell.

4.2.4

Rewards

Raw rewards are defined as a function of the position of the evaluated
agent π and the positions of Good and Evil.
For the rewards, we will work with the notion of trace and the
notion of “cell reward”, that we denote by r(Ci ). Initially, r(Ci ) = 0

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24

complexities, and we analysed whether the obtained results correlated with the measure of difficulty. The results were clear, showing
that the evaluation obtains the expected results in terms of the relation
between expected reward and theoretical problem difficulty. Also, it
showed reasonable differences with other baseline algorithms (e.g.
a random algorithm). All this supported the idea that the test and
the environment class used are on the right direction for evaluating a
specific kind of system. However, the main question was whether the
approach was in the right direction in terms of constructing universal
tests. In other words, it was still necessary to demonstrate if the test
serves to evaluate several kinds of systems and put their results on
the same scale.
In [18] we compared the results of two different systems (humans
and AI algorithms), by using the prototype described in this paper
and the interface for humans. We set both systems to interact with
exactly the same environments. The results, not surprisingly, did not
show the expected difference in intelligence between reinforcement
learning algorithms and humans. This is explained by several reasons. One of them is that the environments were still relatively simple
and reinforcement learning algorithms could still capture and represent all the state matrix for these problems with some partial success.
Another reason is that exercises were independent, so humans could
not reuse what they were learning on some exercises for others, an
issue where humans are supposed to be better than these simple reinforcement algorithms. Also, another possibility is the fact that the
environments had very few agents and the few agents that existed
were not reactive. This makes the state space bounded, which is beneficial for Q-learning. Similarly, the environments had no noise. All
these decisions were made on purpose to keep things simple and also
to be able to formally derive the complexity of the environments. In
general, other explanations can be found as well, since the lack of
other interactive agents can be seen as a lack of social behaviours, as
we explored in subsequent works [12].
Of course, test Λone was just a first prototype which does not incorporate many of the features of an anytime intelligence test and the
measuring framework. Namely, the prototype is not anytime, so the
test does not adapt its complexity to the subject that is evaluating.
Also, we made some simplifications to the environment class, causing objects to lose reactivity. Furthermore, it is very difficult to construct any kind of social behaviour by creating agents from scratch.
These and other issues are being addressed in new prototypes, some
of them under development.

6

CONCLUDING REMARKS

The AN Y NT project aimed at exploring the possibility of formal, universal and feasible tests. As already said, test Λone is just one prototype that does not implement all the features of the theory of anytime
universal tests. However, it is already very informative. For instance,
the experimental results show that the test Λone goes in the right direction, but it still fails to capture some components of intelligence
that should put different kinds of individuals on the right scale.
In defence of test Λone , we have to say that it is quite rare in the
literature to find the same test applied to different kinds of individuals11 . In fact, as argued in [5], relatively simple programs can get
good scores on conventional IQ tests, while small children (with high
potential intelligence) will surely fail. Similarly, illiterate people and
11

The only remarkable exceptions are the works in comparative psychology,
such as [14][15], which are conscious of the difficulties of using the same
test, with different interfaces, for different subjects.

most children would score very badly at the Turing Test, for instance.
And humans are starting to struggle with many CAPTCHAs.
All this means that many feasible and practical tests work because
they are specialised for specific populations. As long as the diversity of subjects is enlarged, measuring intelligence becomes more
difficult and less accurate. As a result, the mere possibility of constructing universal tests is still a hot question. While many may think
that this is irresoluble, we think that unless an answer to this question is found, it will be very difficult (if not impossible) to assess the
diversity of intelligent agents that are envisaged for the forthcoming decades. Being one way or another, there is clearly an ocean of
scientific questions beyond the Turing Test.

ACKNOWLEDGEMENTS
This work was supported by the MEC projects EXPLORAINGENIO
TIN
2009-06078-E,
CONSOLIDER-INGENIO
26706 and TIN 2010-21062-C02-02, and GVA project PROMETEO/2008/051. Javier Insa-Cabrera was sponsored by Spanish
MEC-FPU grant AP2010-4389.

REFERENCES
[1] D. L. Dowe, ‘Foreword re C.S. Wallace’, The Computer Journal, 51(5),
523–560, Christopher Stewart WALLACE (1933–2004) memorial special issue, (2008).
[2] D. L. Dowe and A. R. Hajek, ‘A computational extension to the Turing
Test’, in Proceedings of the 4th Conference of the Australasian Cognitive Science Society, University of Newcastle, NSW, Australia, (1997).
[3] D. L. Dowe and A. R. Hajek, ‘A computational extension
to the Turing Test’, Technical Report #97/322, Dept Computer Science, Monash University, Melbourne, Australia, 9pp,
http://www.csse.monash.edu.au/publications/1997/tr-cs97-322abs.html, (1997).
[4] D. L. Dowe and A. R. Hajek, ‘A non-behavioural, computational extension to the Turing Test’, in International conference on computational
intelligence & multimedia applications (ICCIMA’98), Gippsland, Australia, pp. 101–106, (1998).
[5] D. L. Dowe and J. Hernandez-Orallo, ‘IQ tests are not for machines,
yet’, Intelligence, 40(2), 77–81, (2012).
[6] J. Hernández-Orallo, ‘Beyond the Turing Test’, Journal of Logic, Language and Information, 9(4), 447–466, (2000).
[7] J. Hernández-Orallo, ‘Constructive reinforcement learning’, International Journal of Intelligent Systems, 15(3), 241–264, (2000).
[8] J. Hernández-Orallo, ‘On the computational measurement of intelligence factors’, in Performance metrics for intelligent systems workshop, ed., A. Meystel, pp. 1–8. National Institute of Standards and Technology, Gaithersburg, MD, U.S.A., (2000).
[9] J. Hernández-Orallo, ‘A (hopefully) non-biased universal environment
class for measuring intelligence of biological and artificial systems’, in
Artificial General Intelligence, 3rd International Conference AGI, Proceedings, eds., Marcus Hutter, Eric Baum, and Emanuel Kitzelmann,
“Advances in Intelligent Systems Research” series, pp. 182–183. Atlantis Press, (2010).
[10] J. Hernández-Orallo, ‘On evaluating agent performance in a fixed period of time’, in Artificial General Intelligence, 3rd Intl Conf, ed.,
M. Hutter et al., pp. 25–30. Atlantis Press, (2010).
[11] J. Hernández-Orallo and D. L. Dowe, ‘Measuring universal intelligence: Towards an anytime intelligence test’, Artificial Intelligence,
174(18), 1508–1539, (2010).
[12] J. Hernández-Orallo, D. L. Dowe, S. España-Cubillo, M. V. HernándezLloreda, and J. Insa-Cabrera, ‘On more realistic environment distributions for defining, evaluating and developing intelligence’, in Artificial
General Intelligence 2011, eds., J. Schmidhuber, K.R. Thórisson, and
M. Looks (eds), volume 6830 of LNAI, pp. 82–91. Springer, (2011).
[13] J. Hernández-Orallo and N. Minaya-Collado, ‘A formal definition of
intelligence based on an intensional variant of kolmogorov complexity’,
in In Proceedings of the International Symposium of Engineering of
Intelligent Systems (EIS’98), pp. 146–163. ICSC Press, (1998).

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

26

[14] E. Herrmann, J. Call, M. V. Hernández-Lloreda, B. Hare, and
M. Tomasell, ‘Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis’, Science, Vol 317(5843),
1360–1366, (2007).
[15] E. Herrmann, M. V. Hernández-Lloreda, J. Call, B. Hare, and
M. Tomasello, ‘The structure of individual differences in the cognitive
abilities of children and chimpanzees’, Psychological Science, 21(1),
102, (2010).
[16] M. Hutter, Universal Artificial Intelligence: Sequential Decisions based
on Algorithmic Probability, Springer, 2005.
[17] M. Hutter, ‘Universal algorithmic intelligence: A mathematical
top→down approach’, in Artificial General Intelligence, eds., B. Goertzel and C. Pennachin, Cognitive Technologies, 227–290, Springer,
Berlin, (2007).
[18] J. Insa-Cabrera, D. L. Dowe, S. España-Cubillo, M. V. HernándezLloreda, and J. Hernández-Orallo, ‘Comparing humans and AI agents’,
in Artificial General Intelligence 2011, eds., J. Schmidhuber, K.R.
Thórisson, and M. Looks (eds), volume 6830 of LNAI, pp. 122–132.
Springer, (2011).
[19] J. Insa-Cabrera, D. L. Dowe, and J. Hernández-Orallo, ‘Evaluating a
reinforcement learning algorithm with a general intelligence test’, in
CAEPIA, Advances in Artificial Intelligence, volume 7023 of LNCS,
pp. 1–11. Springer, (2011).
[20] D. Keil and D. Goldin, ‘Indirect interaction in environments for
multi-agent systems’, Environments for Multi-Agent Systems II, 68–87,
(2006).
[21] S. Legg and M. Hutter, ‘A universal measure of intelligence for artificial agents’, in International Joint Conference on Artificial Intelligence,
volume 19, p. 1509, (2005).
[22] S. Legg and M. Hutter, ‘Universal intelligence: A definition of machine intelligence’, Minds and Machines, 17(4), 391–444, (2007).
http://www.vetta.org/documents/UniversalIntelligence.pdf.
[23] L. A. Levin, ‘Universal sequential search problems’, Problems of Information Transmission, 9(3), 265–266, (1973).
[24] M. Li and P. Vitányi, An introduction to Kolmogorov complexity and its
applications (3rd ed.), Springer-Verlag New York, Inc., 2008.
[25] F. Neumann, A. Reichenberger, and M. Ziegler, ‘Variations of the turing
test in the age of internet and virtual reality’, in Proceedings of the 32nd
annual German conference on Advances in artificial intelligence, pp.
355–362. Springer-Verlag, (2009).
[26] P. Sanghi and D. L. Dowe, ‘A computer program capable of passing IQ
tests’, in Proc. 4th ICCS International Conference on Cognitive Science
(ICCS’03), Sydney, Australia, pp. 570–575, (July 2003).
[27] J. Searle, ‘Minds, brains, and programs’, Behavioral and Brain Sciences, 3(3), 417–457, (1980).
[28] R. J. Solomonoff, ‘A formal theory of inductive inference. Part I’, Information and control, 7(1), 1–22, (1964).
[29] A. M. Turing, ‘Computing machinery and intelligence’, Mind, 59, 433–
460, (1950).
[30] J. Veness, K. S. Ng, M. Hutter, and D. Silver, ‘Reinforcement learning
via AIXI approximation’, in Proceedings of the Twenty-Fourth AAAI
Conference on Artificial Intelligence (AAAI-10), pp. 605–611, (2010).
[31] J. Veness, K.S. Ng, M. Hutter, W. Uther, and D. Silver, ‘A Monte
Carlo AIXI Approximation’, Journal of Artificial Intelligence Research, 40(1), 95–142, (2011).
[32] L. Von Ahn, M. Blum, and J. Langford, ‘Telling humans and computers apart automatically’, Communications of the ACM, 47(2), 56–60,
(2004).
[33] C. S. Wallace, Statistical and Inductive Inference by Minimum Message
Length, Ed. Springer-Verlag, 2005.
[34] C. S. Wallace and D. M. Boulton, ‘A information measure for classification’, The Computer Journal, 11(2), 185–194, (1968).
[35] C. S. Wallace and D. L. Dowe, ‘Minimum message length and Kolmogorov complexity’, Computer Journal, 42(4), 270–283, (1999). Special issue on Kolmogorov complexity.
[36] D.A. Washburn and R.S. Astur, ‘Exploration of virtual mazes by rhesus
monkeys ( macaca mulatta )’, Animal Cognition, 6(3), 161–168, (2003).
[37] C.J.C.H. Watkins and P. Dayan, ‘Q-learning’, Mach. learning, 8(3),
279–292, (1992).

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Turing Machines and Recursive Turing Tests
José Hernández-Orallo1 and Javier Insa-Cabrera2 and David L. Dowe3 and Bill Hibbard4
Abstract. The Turing Test, in its standard interpretation, has been
dismissed by many as a practical intelligence test. In fact, it is questionable that the imitation game was meant by Turing himself to be
used as a test for evaluating machines and measuring the progress
of artificial intelligence. In the past fifteen years or so, an alternative
approach to measuring machine intelligence has been consolidating.
The key concept for this alternative approach is not the Turing Test,
but the Turing machine, and some theories derived upon it, such as
Solomonoff’s theory of prediction, the MML principle, Kolmogorov
complexity and algorithmic information theory. This presents an antagonistic view to the Turing test, where intelligence tests are based
on formal principles, are not anthropocentric, are meaningful computationally and the abilities (or factors) which are evaluated can
be recognised and quantified. Recently, however, this computational
view has been touching upon issues which are somewhat related to
the Turing Test, namely that we may need other intelligent agents
in the tests. Motivated by these issues (and others), this paper links
these two antagonistic views by bringing some of the ideas around
the Turing Test to the realm of Turing machines.
Keywords: Turing Test, Turing machines, intelligence, learning,
imitation games, Solomonoff-Kolmogorov complexity.

1

INTRODUCTION

Humans have been evaluated by other humans in all periods of history. It was only in the 20th century, however, that psychometrics was
established as a scientific discipline. Other animals have also been
evaluated by humans, but certainly not in the context of psychometric tests. Instead, comparative cognition is nowadays an important
area of research where non-human animals are evaluated and compared. Machines —yet again differently— have also been evaluated
by humans. However, no scientific discipline has been established for
this.
The Turing Test [31] is still the most popular test for machine intelligence, at least for philosophical and scientific discussions. The
Turing Test, as a measurement instrument and not as a philosophical
argument, is very different to the instruments other disciplines use to
measure intelligence in a scientific way. The Turing Test resembles
a much more customary (and non-scientific) assessment, which happens when humans interview or evaluate other humans (for whatever
1

DSIC, Universitat Politècnica de València, Spain. email:
jorallo@dsic.upv.es
2
DSIC, Universitat Politècnica de València, Spain. email:
jinsa@dsic.upv.es
3 Clayton School of Information Technology, Monash University, Australia.
email: david.dowe@monash.edu
4 Space Science and Engineering Center, University of Wisconsin - Madison,
USA. email: test@ssec.wisc.edu

reason, including, e.g., personnel selection, sports1 or other competitions). The most relevant (and controversial) feature of the Turing
Test is that it takes humans as a touchstone to which machines should
be compared. In fact, the comparison is not performed by an objective criterion, but assessed by human judges, which is not without
controversy. Another remarkable feature (and perhaps less controversial) is that the Turing Test is set on an intentionally restrictive
interaction channel: a teletype conversation. Finally, there are some
features about the Turing Test which make it more general than other
kinds of intelligence tests. For instance, it is becoming increasingly
better known that programs can do well at human IQ tests [32][8],
because ordinary IQ tests only evaluate narrow abilities and assume
that narrow abilities accurately reflect human abilities across a broad
set of tasks, which may not hold for non-human populations. The
Turing test (and some formal intelligence measures we will review
in the following section) can test broad sets of tasks.
We must say that Turing cannot be blamed for all the controversy.
The purpose of Turing’s imitation game [37] was to show that intelligence could be assessed and recognised in a behavioural way, without the need for directly measuring or recognising some other physical or mental issues such as thinking, consciousness, etc. In Turing’s
view, intelligence can be just seen as a cognitive ability (or property)
that some machines might have and others might not. In fact, the
standard scientific view should converge to defining intelligence as
an ability that some systems: humans, non-human animals, machines
—and collectives thereof—, might or might not have, or, more precisely, might have to a larger or lesser degree. This view has clearly
been spread by the popularity of psychometrics and IQ tests.2
While there have been many variants and extensions of the Turing Test (see [33] or [31] for an account of these), none of them
(and none of the approaches in psychometrics and animal cognition,
either) have provided a formal, mathematical definition of what in1

2

In many sports, to see how good a player is, we want competent judges but
also appropriate team-mates and opponents. Good tournaments and competitions are largely designed so as to return (near) maximal expected information.
In fact, the notion of consciousness and other phenomena is today better
separated from intelligence than it was sixty years ago. They are now seen
as related but different things. For instance, nobody doubts that a team of
people can score well in a single IQ test (working together). In fact, the
team, using a teletype communication as in the Turing Test, can dialogue,
write poetry, make jokes, do complex mathematics and all these human
things. They can even do these things continuously for days or weeks, while
some of the particular individuals rest, eat, go to sleep, die, etc. Despite
all of this happening on the other side of the teletype communication, the
system is just regarded as one subject. So the fact that we can effectively
measure the cognitive abilities of the team or even make the team pass the
Turing Test does not lead us directly to statements such as ‘the team has a
mind’ or ‘the team is conscious’. At most, we say this in a figurative sense,
as we use it for the collective consciousness of a company or country. In the
end, the ‘team of people’ is one of the best arguments against Searle’s Chinese room and a good reference whenever we are thinking about evaluating
intelligence.

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telligence is and how it can be measured.
A different approach is based on one of the things that the Turing Test is usually criticised for: learning3 . This alternative approach requires a proper definition of learning, and actual mechanisms for measuring learning ability. Interestingly, the answer to this
is given by notions devised from Turing machines. In the 1960s, Ray
Solomonoff ‘solved’ the problem of induction (and the related problems of prediction and learning) [36] by the use of Turing machines.
This, jointly with the theory of inductive inference given by the Minimum Message Length (MML) principle [39, 40, 38, 5], algorithmic
information theory [1], Kolmogorov complexity [25, 36] and compression theory, paved the way in the 1990s for a new approach for
defining and measuring intelligence based on algorithmic information theory. This approach will be summarised in the next section.
While initially there was some connection to the Turing Test, this
line of research has been evolving and consolidating in the past fifteen years (or more), cutting all the links to the Turing Test. This has
provided important insights into what intelligence is and how it can
be measured, and has given clues to the (re-)understanding of other
areas where intelligence is defined and measured, such as psychometrics and animal cognition.
An important milestone of this journey has been the recent realisation in this context that (social) intelligence is the ability to perform
well in an environment full of other agents of similar intelligence.
This is a consequence of some experiments which show that when
performance is measured in environments where no other agents coexist, some important traits of intelligence are not fully recognised. A
solution for this has been formalised as the so-called Darwin-Wallace
distribution of environments (or tasks) [18]. The outcome of all this is
that it is increasingly an issue whether intelligence might be needed
to measure intelligence. But this is not because we might need intelligent judges as in the Turing Test, but because we may need other
intelligent agents to become part of the exercises or tasks an intelligence test should contain (as per footnote 1).
This seems to take us back to the Turing Test, a point some of us
deliberately abandoned more than fifteen years ago. Re-visiting the
Turing Test now is necessarily very different, because of the technical companions, knowledge and results we have gathered during this
journey (universal Turing machines, compression, universal distributions, Solomonoff-Kolmogorov complexity, MML, reinforcement
learning, etc.).
The paper is organised as follows. Section 2 introduces a short account of the past fifteen years concerning definitions and tests of machine intelligence based on (algorithmic) information theory. It also
discusses some of the most recent outcomes and positions in this line,
which have led to the notion of Darwin-Wallace distribution and the
need for including other intelligent agents in the tests, suggesting an
inductive (or recursive, or iterative) test construction and definition.
This is linked to the notion of recursive Turing Test (see [32, sec.
5.1] for a first discussion on this). Section 3 analyses the base case
by proposing several schemata for evaluating systems that are able
to imitate Turing machines. Section 4 defines different ways of doing the recursive step, inspired by the Darwin-Wallace distribution
and ideas for making this feasible. Section 5 briefly explores how all
this might develop, and touches upon concepts such as universality
in Turing machines and potential intelligence, as well as some sug3

This can be taken as further evidence for Turing not conceiving the imitation test as an actual test for intelligence, because the issue about machines
being able to learn was seen as inherent to intelligence for Turing [37, section 7], and yet the Turing Test is not especially good at detecting learning
ability during the test.

gestions as to how machine intelligence measurement might develop
in the future.

2

MACHINE INTELLIGENCE
MEASUREMENT USING TURING
MACHINES

There are, of course, many proposals for intelligence definitions and
tests for machines which are not based on the Turing Test. Some
of them are related to psychometrics, some others may be related
to other areas of cognitive science (including animal cognition) and
some others originate from artificial intelligence (e.g., some competitions running on specific tasks such as planning, robotics, games,
reinforcement learning, . . . ). For an account of some of these, the
reader can find a good survey in [26]. In this section, we will focus
on approaches which use Turing machines (and hence computation)
as a basic component for the definition of intelligence and the derivation of tests for machine intelligence.
Most of the views of intelligence in computer science are sustained over a notion of intelligence as a special kind of information processing. The nature of information, its actual content and
the way in which patterns and structure can appear in it can only
be explained in terms of algorithmic information theory. The Minimum Message Length (MML) principle [39, 40] and SolomonoffKolmogorov complexity [36, 25] capture the intuitive notion that
there is structure –or redundancy– in data if and only if it is compressible, with the relationship between MML and (two-part) Kolmogorov complexity articulated in [40][38, chap. 2][5, sec. 6]. While
Kolmogorov [25] and Chaitin [1] were more concerned with the notions of randomness and the implications of all this in mathematics
and computer science, Solomonoff [36] and Wallace [39] developed
the theory with the aim of explaining how learning, prediction and inductive inference work. In fact, Solomonoff is said to have ‘solved’
the problem of induction [36] by the use of Turing machines. He was
also the first to introduce the notions of universal distribution (as the
distribution of strings given by a UTM from random input) and the
invariance theorem (which states that the Kolmogorov complexity of
a string calculated with two different reference machines only differs
by a constant which is independent of the string).
Chaitin briefly made mention in 1982 of the potential relationship
between algorithmic information theory and measuring intelligence
[2], but actual proposals in this line did not start until the late 1990s.
The first proposal was precisely introduced over a Turing Test and
as a response to Searle’s Chinese room [35], where the subject was
forced to learn. This induction-enhanced Turing Test [7][6] could
then evaluate a general inductive ability. The importance was not that
any kind of ability could be included in the Turing Test, but that this
ability could be formalised in terms of MML and related ideas, such
as (two-part) compression.
Independently and near-simultaneously, a new intelligence test
(C-test) [19] [12] was derived as sequence prediction problems
which were generated by a universal distribution [36]. The difficulty of the exercises was mathematically derived from a variant of
Kolmogorov complexity, and only exercises with a certain degree of
difficulty were included and weighted accordingly. These exercises
were very similar to those found in some IQ tests, but here they were
created from computational principles. This work ‘solved’ the traditional subjectivity objection of the items in IQ tests, i.e., since the
continuation of each sequence was derived from its shortest explanation. However, this test only measured one cognitive ability and
its presentation was too narrow to be a general test. Consequently,

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29

these ideas were extended to other cognitive abilities in [14] by the
introduction of other ‘factors’, and the suggestion of using interactive tasks where “rewards and penalties could be used instead”, as in
reinforcement learning [13].
Similar ideas followed relating compression and intelligence.
Compression tests were proposed as a test for artificial intelligence
[30], arguing that “optimal text compression is a harder problem than
artificial intelligence as defined by Turing’s”. Nonetheless, the fact
that there is a connection between compression and intelligence does
not mean that intelligence can be just defined as compression ability
(see, e.g., [9] for a full discussion on this).
Later, [27] would propose a notion which they referred to as a
“universal intelligence measure” —universal because of its proposed
use of a universal distribution for the weighting over environments.
The innovation was mainly their use of a reinforcement learning setting, which implicitly accounted for the abilities not only of learning
and prediction, but also of planning. An interesting point for making
this proposal popular was its conceptual simplicity: intelligence was
just seen as average performance in a range of environments, where
the environments were just selected by a universal distribution.
While innovative, the universal intelligence measure [27] showed
several shortcomings stopping it from being a viable test. Some of
the problems are that it requires a summation over infinitely many
environments, it requires a summation over infinite time within each
environment, Kolmogorov complexity is typically not computable,
disproportionate weight is put on simple environments (e.g., with 1−
2−7 > 99% of weight put on environments of size less than 8, as also
pointed out by [21]), it is (static and) not adaptive, it does not account
for time or agent speed, etc
Hernandez-Orallo and Dowe [17] re-visited this to give an intelligence test that does not have these abovementioned shortcomings.
This was presented as an anytime universal intelligence test. The
term universal here was used to designate that the test could be applied to any kind of subject: machine, human, non-human animal or
a community of these. The term anytime was used to indicate that
the test could evaluate any agent speed, it would adapt to the intelligence of the examinee, and that it could be interrupted at any time to
give an intelligence score estimate. The longer the test runs, the more
reliable the estimate (the average reward [16]).
Preliminary tests have since been done [23, 24, 28] for comparing
human agents with non-human AI agents. These tests seem to succeed in bringing theory to practice quite seamlessly and are useful
to compare the abilities of systems of the same kind. However, there
are some problems when comparing systems of different kind, such
as human and AI algorithms, because the huge difference of both
(with current state-of-the-art technology) is not clearly appreciated.
One explanation for this is that (human) intelligence is the result of
the adaptation to environments where the probability of other agents
(of lower or similar intelligence) being around is very high. However,
the probability of having another agent of even a small degree of intelligence just by the use of a universal distribution is discouragingly
remote. Even in environments where other agents are included on
purpose [15], it is not clear that these agents properly represent a rich
‘social’ environment. In [18], the so-called Darwin-Wallace distribution is introduced where environments are generated using a universal distribution for multi-agent environments, and where a number of
agents that populate the environment are also generated by a universal distribution. The probability of having interesting environments
and agents is very low on this first ‘generation’. However, if an intelligence test is administered to this population and only those with
a certain level are preserved, we may get a second population whose

agents will have a slightly higher degree of intelligence. Iterating this
process we have different levels for the Darwin-Wallace distribution,
where evolution is solely driven (boosted) by a fitness function which
is just measured by intelligence tests.

3

THE BASE CASE: THE TURING TEST FOR
TURING MACHINES

A recursive approach can raise the odds for environments and tasks
of having a behaviour which is attributed to more intelligent agents.
This idea of recursive populations can be linked to the notion of recursive Turing Test [32, sec. 5.1], where the agents which have succeeded at lower levels could be used to be compared at higher levels.
However, there are many interpretations of this informal notion of a
recursive Turing Test. The fundamental idea is to eliminate the human reference from the test using recursion —either as the subject
that has to be imitated or the judge which is used to tell between the
subjects.
Before giving some (more precise) interpretations of a recursive
version of the Turing Test, we need to start with the base case, as
follows (we use TM and UTM for Turing Machine and Universal
Turing Machine respectively):
Definition 1 The imitation game for Turing machines4 is defined as
a tuple "D, B, C, I#
• The reference subject A is randomly taken as a TM using a distribution D.
• Subject B (the evaluee) tries to emulate A.
• The similarity between A and B is ‘judged’ by a criterion or judge
C through some kind of interaction protocol I. The test returns this
similarity.
An instance of the previous schema requires us to determine the distribution D and the similarity criterion C and, most especially, how
the interaction I goes. In the classical Turing Test, we know that D is
the human population, C is given by a human judge, and the interaction is an open teletype conversation5 . Of course, other distributions
for D could lead to other tests, such as, e.g., a canine test, taking
D as a dog population, and judges as other dogs which have to tell
which is the member of the species or perhaps even how intelligent
it is (for whatever purpose —e.g., mating or idle curiosity).
More interestingly, one possible instance for Turing machines
could go as follows. We can just take D as a universal distribution
over a reference UTM U , so p(A) = 2−KU (A) , where KU (A) is the
prefix-free Kolmogorov complexity of A relative to U . This means
that simple reference subjects have higher probability than complex
subjects. Interaction can go as follows. The ‘interview’ consists of
questions as random finite binary strings using a universal distribution s1 , s2 , ... over another reference UTM, V . The test starts by subjects A and B receiving string s1 and giving two sequences a1 and b1
as respective answers. Agent B will also receive what A has output
4

5

The use of Turing machines for the reference subject is relevant and not
just a way to link two things by their name, Turing. Turing machines are
required because we need to define formal distributions on them, and this
cannot be done (at least theoretically) for humans, or animals or ‘agents’.
This free teletype conversation may be problematic in many ways. Typically, the judge C wishes to steer the conversation in directions which will
enable her to get (near-)maximal (expected) information (before the timelimit deadline of the test) about whether or not the evaluee subject B is
or is not from D. One tactic for a subject which is not from D (and not a
good imitator either) is to distract the judge C and steer the conversation in
directions which will give judge C (near-) minimal (expected) information.

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30

immediately after this. Judge C is just a very simple function which
compares whether a1 and b1 are equal. After one interation, the system issues string s2 . After several iterations, the score (similarity)
given to B is calculated as an aggregation of the times ai and bi have
been equal.
This can be seen as formalisation of the Turing Test where it is a
Turing machine that needs to be imitated, and the criterion for imitation is the similarity between the answers given by A and B to the
same questions. If subject B cannot be told or instructed about the
goal of the test (imitating A) then we can use rewards after each step,
possibly concealing A’s outputs from B as well.
This test might seem ridiculous at first sight. Some might argue
that being able to imitate a randomly-chosen TM is not related to
intelligence. However, two issues are important here. First, agent B
does not know who A is in advance. Second, agent B tries to imitate
A solely from its behaviour.
This makes the previous version of the test very similar to the most
abstract setting used for analysing what learning is, how much complexity it has and whether it can be solved. First, this is tantamount to
Gold’s language identification in the limit [11]. If subject B is able to
identify A at some point, then it will start to score perfectly from that
moment. While Gold was interested in whether this could be done in
general and for every possible A, here we are interested in how well
B does this on average for a randomly-chosen A from a distribution.
In fact, many simple TMs can be identified quite easily, such as those
simple TMs which output the same string independently of the input.
Second, and following this averaging approach, Solomonoff’s setting
is also very similar to this. Solomonoff proved that B could get the
best estimations for A if B used a mixture of all consistent models
inversely weighted by 2 to the power of their Kolmogorov complexity. While this may give the best theoretical approach for prediction
and perhaps for “imitation”, it does not properly “identify” A. Identification can only be properly claimed if we have one single model
of A which is exactly as A. This distinction between one vs. multiple models is explicit in the MML principle, which usually considers
just one single model, the one with the shortest two-part message
encoding of said model followed by the data given this model.
There is already an intelligence test which corresponds to the previous instance of definition 1, the C-test, mentioned above. The Ctest measures how well an agent B is able to identify the pattern
behind a series of sequences (each sequence is generated by a different program, i.e., a different Turing machine). The C-test does not
use a query-answer setting, but the principles are the same.
We can develop a slight modification of definition 1 by considering that subject A also tries to imitate B. This might lead to easy
convergence in many cases (for relatively intelligent A and B) and
would not be very useful for comparing A and B effectively. A significant step forward is when we consider that the goal of A is to
make outputs that cannot be imitated by B. While it is clearly different, this is related to some versions of Turing’s imitation game,
where one of the human subjects pretends to be a machine. While
there might be some variants here to explore, if we restrict the size of
the strings used for questions and answers to 1 (this makes agreeing
and disagreeing equally likely), this is tantamount to the game known
as ‘matching pennies’ (a binary version of rock-paper-scissors where
the first player has to match the head or tail of the second player, and
the second player has to disagree on the head or tail of the first). Interestingly, this game has also been proposed as an intelligence test
in the form of Adversarial Sequence Prediction [20][22] and is related to the “elusive model paradox” [3, footnote 211][4, p 455][5,
sec. 7.5].

This instance makes it more explicit that the distribution D over
the agents that the evaluee has to imitate or compete with is crucial.
In the case of imitation, however, there might be non-intelligent Turing machines which are more difficult to imitate/identify than many
intelligent Turing machines, and this difficulty seems to be related
to the Kolmogorov complexity of the Turing machine. And linking
difficulty to Kolmogorov complexity is what the C-test does. But biological intelligence is frequently biased to social environments, or
at least to environments where other agents can be around eventually. In fact, societies are usually built on common sense and common understanding, but in humans this might be an evolutionarilyacquired ability to imitate other humans, but not other intelligent
beings in general. Some neurobiological structures, such as mirror
neurons have been found in primates and other species, which may
be responsible of understanding what other people do and will do,
and for learning new skills by imitation. Nonetheless, we must say
that human unpredictability is frequently impressive, and its relation
to intelligence is far from being understood. Interestingly, some of
the first analyses on this issue [34][29] linked the problem with the
competitive/adversarial scenario, which is equivalent to the matching pennies problem, where the intelligence of the peer is the most
relevant feature (if not the only one) for assessing the difficulty of
the game, as happens in most games. In fact, matching pennies is
the purest and simplest game, since it reduces the complexity of the
‘environment’ (rules of the game) to a minimum.

4

RECURSIVE TURING TESTS FOR TURING
MACHINES

The previous section has shown that introducing agents (in this case,
agent A) in a test setting requires a clear assessment of the distribution which is used for introducing them. A general expression of how
to make a Turing Test for Turing machines recursive is as follows:
Definition 2 The recursive imitation game for Turing machines is
defined as a tuple !D, C, I" where tests and distributions are obtained as follows:
1. Set D0 = D and i = 0.
2. For each agent B in a sufficiently large set of TMs
3.
Apply a sufficiently large set of instances of definition 1 with
parameters !Di , B, C, I".
4.
B’s intelligence at degree i is averaged from this sample of
imitation tests.
5. End for
6. Set i = i + 1
7. Calculate a new distribution Di where each TM has a probability
which is directly related to its intelligence at level i − 1.
8. Go to 2
This gives a sequence of Di .
The previous approach is clearly uncomputable in general, and still
intractable even if reasonable samples, heuristics and step limitations
are used. A better approach to the problem would be some kind of
propagation system, such as Elo’s rating system of chess [10], which
has already been suggested in some works and competitions in artificial intelligence. A combination of a soft universal distribution,
where simple agents would have slightly higher probability, and a
one-vs-one credit propagation system such as Elo’s rating (or any
other mechanism which returns maximal expected information with
a minimum of pairings), could feasibly aim at having a reasonably

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31

good estimate of the relative abilities of a big population of Turing
machines, including some AI algorithms amongst them.
What would this rating mean? If we are using the imitation game, a
high rating would show that the agent is able to imitate/identify other
agents of lower rating well and that it is a worse imitator/identifier
than other agents with higher rating. However, there is no reason to
think that the relations are transitive and anti-reflexive; e.g., it might
even happen that an agent with very low ranking would be able to
imitate an agent with very high ranking better than the other way
round.
One apparently good thing about this recursion and rating system
is that the start-up distribution can be very important from the point
of view of heuristics, but it might be less important for the final result. This is yet another way of escaping from the problems of using a
universal distribution for environments or agents, because very simple things take almost all the probability —as per section 2. Using
difficulty as in the C-test, making adaptive tests such as the anytime
test, setting a minimum complexity value [21] or using hierarchies
of environments [22] where “an agent’s intelligence is measured as
the ordinal of the most difficult set of environments it can pass” are
solutions for this. We have just seen another possible solution where
evaluees (or similar individuals) can take part in the tests.

5

DISCUSSION

The Turing test, in some of its formulations, is a game where an agent
tries to imitate another (or its species or population) which might
(or might not) be cheating. If both agents are fair, and we do not
consider any previous information about the agents (or their species
or populations), then we have an imitation test for Turing machines.
If one is cheating, we get closer to the adversarial case we have also
seen.
Instead of including agents arbitrarily or assuming that any agent
has a level of intelligence a priori, a recursive approach is necessary.
This is conceptually possible, as we have seen, although its feasible
implementation needs to be carefully considered, possibly in terms
of rankings after random 1-vs-1 comparisons.
This view of the (recursive) Turing test in terms of Turing machines has allowed us to connect the Turing test with fundamental issues in computer science and artificial intelligence, such as the problem of learning (as identification), Solomonoff’s theory of prediction,
the MML principle, game theory, etc. These connections go beyond
to other disciplines such as (neuro-)biology, where the role of imitation and adversarial prediction are fundamental, such as predatorprey games, mirror neurons, common coding theory, etc. In addition,
this has shown that the line of research with intelligence tests derived
from algorithmic information theory and the recent Darwin-Wallace
distribution are also closely related to this as well. This (again) links
this line of research to the Turing test, where humans have been replaced by Turing machines.
This sets up many avenues for research and discussion. For instance, the idea that the ability of imitating relates to intelligence can
be understood in terms of the universality of a Turing machine, i.e.
the ability of a Turing machine to emulate another. If a machine can
emulate another, it can acquire all the properties of the latter, including intelligence. However, in this paper we have referred to the notion
of ‘imitation’, which is different to the concept of Universal Turing
machine, since a UTM is defined as a machine such that there is an
input that turns it into any other pre-specified Turing machine. A machine which is able to imitate well is a good learner, which can finally
identify any pattern on the input and use it to imitate the source. In

fact, a good imitator is, potentially, very intelligent, since it can, in
theory (and disregarding efficiency issues), act as any other very intelligent being by just observing its behaviour. Turing advocated for
learning machines in section 7 of the very same paper [37] where he
introduced the Turing Test. Solomonoff taught us what learning machines should look like. We are still struggling to make them work in
practice and preparing for assessing them.

ACKNOWLEDGEMENTS
This work was supported by the MEC projects EXPLORAINGENIO
TIN
2009-06078-E,
CONSOLIDER-INGENIO
26706 and TIN 2010-21062-C02-02, and GVA project PROMETEO/2008/051. Javier Insa-Cabrera was sponsored by Spanish
MEC-FPU grant AP2010-4389.

REFERENCES
[1] G. J. Chaitin, ‘On the length of programs for computing finite sequences’, Journal of the Association for Computing Machinery, 13,
547–569, (1966).
[2] G. J. Chaitin, ‘Godel’s theorem and information’, International Journal
of Theoretical Physics, 21(12), 941–954, (1982).
[3] D. L. Dowe, ‘Foreword re C. S. Wallace’, Computer Journal, 51(5),
523 – 560, (September 2008). Christopher Stewart WALLACE (19332004) memorial special issue.
[4] D. L. Dowe, ‘Minimum Message Length and statistically consistent invariant (objective?) Bayesian probabilistic inference - from (medical)
“evidence”’, Social Epistemology, 22(4), 433 – 460, (October - December 2008).
[5] D. L. Dowe, ‘MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness’, in Handbook of the
Philosophy of Science - Volume 7: Philosophy of Statistics, ed., P. S.
Bandyopadhyay and M. R. Forster, pp. 901–982. Elsevier, (2011).
[6] D. L. Dowe and A. R. Hajek, ‘A non-behavioural, computational extension to the Turing Test’, in Intl. Conf. on Computational Intelligence &
multimedia applications (ICCIMA’98), Gippsland, Australia, pp. 101–
106, (February 1998).
[7] D. L. Dowe and A. R. Hajek, ‘A computational extension to the Turing
Test’, in Proceedings of the 4th Conference of the Australasian Cognitive Science Society, University of Newcastle, NSW, Australia, (September 1997).
[8] D. L. Dowe and J. Hernandez-Orallo, ‘IQ tests are not for machines,
yet’, Intelligence, 40(2), 77–81, (2012).
[9] D. L. Dowe, J. Hernández-Orallo, and P. K. Das, ‘Compression and intelligence: social environments and communication’, in Artificial General Intelligence, eds., J. Schmidhuber, K.R. Thórisson, and M. Looks,
volume 6830, pp. 204–211. LNAI series, Springer, (2011).
[10] A.E. Elo, The rating of chessplayers, past and present, volume 3, Batsford London, 1978.
[11] E.M. Gold, ‘Language identification in the limit’, Information and control, 10(5), 447–474, (1967).
[12] J. Hernández-Orallo, ‘Beyond the Turing Test’, J. Logic, Language &
Information, 9(4), 447–466, (2000).
[13] J. Hernández-Orallo, ‘Constructive reinforcement learning’, International Journal of Intelligent Systems, 15(3), 241–264, (2000).
[14] J. Hernández-Orallo, ‘On the computational measurement of intelligence factors’, in Performance metrics for intelligent systems workshop, ed., A. Meystel, pp. 1–8. National Institute of Standards and Technology, Gaithersburg, MD, U.S.A., (2000).
[15] J. Hernández-Orallo, ‘A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial
systems’, in Artificial General Intelligence, 3rd Intl Conf, ed.,
M. Hutter et al., pp. 182–183. Atlantis Press, Extended report at
http://users.dsic.upv.es/proy/anynt/unbiased.pdf, (2010).
[16] J. Hernández-Orallo, ‘On evaluating agent performance in a fixed period of time’, in Artificial General Intelligence, 3rd Intl Conf, ed.,
M. Hutter et al., pp. 25–30. Atlantis Press, (2010).
[17] J. Hernández-Orallo and D. L. Dowe, ‘Measuring universal intelligence: Towards an anytime intelligence test’, Artificial Intelligence
Journal, 174, 1508–1539, (2010).

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

32

[18] J. Hernández-Orallo, D. L. Dowe, S. España-Cubillo, M. V. HernándezLloreda, and J. Insa-Cabrera, ‘On more realistic environment distributions for defining, evaluating and developing intelligence’, in Artificial General Intelligence, eds., J. Schmidhuber, K.R. Thórisson, and
M. Looks, volume 6830, pp. 82–91. LNAI, Springer, (2011).
[19] J. Hernández-Orallo and N. Minaya-Collado, ‘A formal definition of intelligence based on an intensional variant of Kolmogorov complexity’,
in Proc. Intl Symposium of Engineering of Intelligent Systems (EIS’98),
pp. 146–163. ICSC Press, (1998).
[20] B. Hibbard, ‘Adversarial sequence prediction’, in Proceeding of the
2008 conference on Artificial General Intelligence 2008: Proceedings
of the First AGI Conference, pp. 399–403. IOS Press, (2008).
[21] B. Hibbard, ‘Bias and no free lunch in formal measures of intelligence’,
Journal of Artificial General Intelligence, 1(1), 54–61, (2009).
[22] B. Hibbard, ‘Measuring agent intelligence via hierarchies of environments’, Artificial General Intelligence, 303–308, (2011).
[23] J. Insa-Cabrera, D. L. Dowe, S. España-Cubillo, M. Victoria
Hernández-Lloreda, and José Hernández-Orallo, ‘Comparing humans
and ai agents’, in AGI: 4th Conference on Artificial General Intelligence - Lecture Notes in Artificial Intelligence (LNAI), volume 6830,
pp. 122–132. Springer, (2011).
[24] J. Insa-Cabrera, D. L. Dowe, and José Hernández-Orallo, ‘Evaluating
a reinforcement learning algorithm with a general intelligence test’, in
CAEPIA - Lecture Notes in Artificial Intelligence (LNAI), volume 7023,
pp. 1–11. Springer, (2011).
[25] A. N. Kolmogorov, ‘Three approaches to the quantitative definition of
information’, Problems of Information Transmission, 1, 4–7, (1965).
[26] S. Legg and M. Hutter, ‘Tests of machine intelligence’, in 50 years of
artificial intelligence, pp. 232–242. Springer-Verlag, (2007).
[27] S. Legg and M. Hutter, ‘Universal intelligence: A definition of machine
intelligence’, Minds and Machines, 17(4), 391–444, (November 2007).
[28] S. Legg and J. Veness, ‘An Approximation of the Universal Intelligence Measure’, in Proceedings of Solomonoff 85th memorial conference. Springer, (2012).
[29] D. K. Lewis and J. Shelby-Richardson, ‘Scriven on human unpredictability’, Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 17(5), 69 – 74, (October 1966).
[30] M. V. Mahoney, ‘Text compression as a test for artificial intelligence’,
in Proceedings of the National Conference on Artificial Intelligence,
AAAI, pp. 970–970, (1999).
[31] G. Oppy and D. L. Dowe, ‘The Turing Test’, in Stanford Encyclopedia of Philosophy, ed., Edward N. Zalta. Stanford University, (2011).
http://plato.stanford.edu/entries/turing-test/.
[32] P. Sanghi and D. L. Dowe, ‘A computer program capable of passing IQ
tests’, in 4th Intl. Conf. on Cognitive Science (ICCS’03), Sydney, pp.
570–575, (2003).
[33] A.P. Saygin, I. Cicekli, and V. Akman, ‘Turing test: 50 years later’,
Minds and Machines, 10(4), 463–518, (2000).
[34] M. Scriven, ‘An essential unpredictability in human behavior’, in Scientific Psychology: Principles and Approaches, eds., B. B. Wolman and
E. Nagel, 411–425, Basic Books (Perseus Books), (1965).
[35] J. R. Searle, ‘Minds, brains and programs’, Behavioural and Brain Sciences, 3, 417–457, (1980).
[36] R. J. Solomonoff, ‘A formal theory of inductive inference’, Information
and Control, 7, 1–22, 224–254, (1964).
[37] A. M. Turing, ‘Computing machinery and intelligence’, Mind, 59, 433–
460, (1950).
[38] C. S. Wallace, Statistical and Inductive Inference by Minimum Message
Length, Information Science and Statistics, Springer Verlag, May 2005.
ISBN 0-387-23795X.
[39] C. S. Wallace and D. M. Boulton, ‘An information measure for classification’, Computer Journal, 11(2), 185–194, (1968).
[40] C. S. Wallace and D. L. Dowe, ‘Minimum message length and Kolmogorov complexity’, Computer Journal, 42(4), 270–283, (1999).

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What language for Turing Test in the age of qualia?
Francesco Bianchini1, Domenica Bruni2
Abstract. What is the most relevant legacy by Turing for
epistemology of Artificial Intelligence (AI) and cognitive
science? Of course, we could see it in the ideas set out in his
well-known article of 1950, Computing Machinery and
Intelligence. But how could his imitation game, and its following
evolution in what we know as Turing Test, still be so relevant?
What we want to argue is that the nature of imitation game as a
method for evaluating research on intelligent artifacts, has not its
core specifically in (natural) language capability as a way of
showing the presence of intelligence in a certain entity, but in the
interaction between human being and machines. Humancomputer interaction is a particular field in information science
for many important practical respects, but interaction between
human being and machines is the deepest sense of Turing’s ideas
on evaluation of intelligent behavior and entities, within and
beyond its connection with natural language. And from this point
of view it could be methodologically and epistemologically
useful for further research in every discipline involving machine
and artificial artifacts, especially as concerns the very current
subject of consciousness and qualia. In what follows we will try
to argue such a perspective by showing some field in which
interaction, in connection with different sorts of language, could
be of interest in the spirit of Turing’s 1950 article.12

1 TURING, LANGUAGE AND INTERACTION
One of the most interesting idea by Turing was a based-onlanguage test for proving the intelligence, or the intelligent
behavior, of a program. In Turing’s terms, it is a machine
showing an autonomous and self-produced intelligent behavior.
Actually, Turing never spoke about a test, but just about an
imitation game, using the concept of imitation as an intuitive
concept. This is a typical way of thinking as regards Turing,
though, who had provided a method for catching the notion of
computable function in a mechanical way through a set of
intuitive concepts about fifteen years before [24]. Likewise the
case of computation theory, the Turing’s aim in 1950 article was
to deal with a very notable subject in the easiest and most
straightforward manner, and avoiding the involvement with
more complex and specific theoretical structures based on fielddependent notions.
In the case of imitation game the combination of the notion of
“imitation” and of the use of natural language allowed Turing to
express a paradigmatic method for evaluating artificial products,
but gave rise as well to an endless debate all over the last sixty
years about the suitableness of this kind of testing artificial
intelligence. Leaving aside the problem concerning the correct
1

Dept. of Philosophy, University of Bologna. Email:
francesco.bianchini5@unibo.it
2
Dept. of Cognitive Science, University of Messina, Email:
dbruni@unime.it

interpretation of the notion of “imitation”, we may ask first
whether the role of language in the test is fundamental or it is
just connected to the spirit of the period in which Turing wrote
his paper, that is within the current behaviorist paradigm in
psychology and in the light of the natural language centrality in
the philosophy of twentieth century. In other terms, why did
Turing choose natural language in order to build a general frame
for evaluating the intelligence of artificial, programmed
artifacts? Is such a way of thinking (and researching) still useful?
And, if so, what can we say about it in relation with further
research in this field?
As we said, the choice of natural language had the purpose to
put the matter in an intuitive manner. We human beings usually
ascribe intelligence to other human beings through linguistic
conversations, mostly carrying out in a question-answer form.
Besides, Turing himself asserts in 1950 article that such a
method «has the advantage of drawing a fairly sharp line
between the physical and the intellectual capacities of a man»
[26]. This is the ordinary explanation of Turing’s choice. But it
is also true that, in a certain sense, the very first enunciation of
the imitation game is in another previous work by Turing, where,
ending his exposition on machine intelligence, he speaks about a
«little experiment» regarding the possibility of a chess game
between two human beings (A and C), and between a human
being (A) and a paper machine worked by a human being (B).
Turing asserts that if «two rooms are used with some
arrangement for communicating moves, and a game is played
between C and either A or the paper machine […] C may find it
quite difficult to tell which he is playing. (This is a rather
idealized form of an experiment I have actually done.)» [25].
Such a brief sketch of the imitation game in 1948 paper is not
surprising because that paper is a sort of first draft of the
Turing’s ideas of 1950 paper, and it is even more considerable
for some remarks, for example, on self-organizing machines or
on the possibility of machine learning. Moreover, it is not
surprising that Turing speaks about machines referring to them
as paper machines, namely just for their logical, abstract
structure. It is another main Turing’s theme, that remembers the
human computor of 1936 paper. What is interesting is the fact
that the first, short outline of imitation game is not based on
language, but on a subject that is more early-artificialintelligence-like, that is, chess game. So, (natural) language is
not necessary for imitation game from the point of view of
Turing, and yet the ordinary explanation of Turing’s choice for
language is still valid within such a framework. In other terms,
Turing was aware not only that there are other domains in which
a machine can apply itself autonomously – a trivial fact – but
also that such domains are as enough good as natural language
for imitation game. Nevertheless, he choose natural language as
paradigmatic.
What conclusions can we draw from such remarks? Probably
two ones. First, Turing was pretty definitely aware that the
evaluation of artificial intelligence (AI) products, in a broad

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34

sense, would be a very difficult subject, maybe the more
fundamental as regards the epistemology of AI and cognitive
science, even if, obviously, he didn’t use such terms in 1950.
Secondly, that the choice of language and the role of language in
imitation game are even more subtle than the popular culture and
the AI tradition usually assert. As a matter of fact, he did not
speak about natural language in general but of a “questionanswer method”, a method that involves communication, not just
language processing or producing. So, from this point of view it
seems that, for Turing, natural language processing or producing
are just some peculiar human cognitive abilities among many
other ones, and are not basic for testing intelligence. What is
basic for such a task is communication or, to use another, more
inclusive term, interaction. But a specification is needed. We are
not maintaining that the capability of using language is not a
cognitive feature, but that in Turing’s view interaction is the best
way in order to detect intelligence, and language interaction, by
means of question-answer method, is perhaps the most intuitive
form of interaction for human beings. No interaction is
tantamount to no possibility to identify intelligence, and for such
a purpose one of the two poles of interaction must be a human
being3.
Furthermore, the «question and answer method seems to be
suitable for introducing almost anyone of the fields of human
endeavour that we wish to include» [26] and, leaving aside the
above-mentioned point concerning the explicit Turing’s request
to penalize in no way machines or human beings for their
unshared features, we could consider it as the main aim of
Turing, namely generalizing the intelligence testing. Of course,
such an aim anticipates one of the mainstream of the following
rising AI4, but it has an even wider range. Turing was not
speaking, indeed, about problem solving, but trying to formulate
a criterion and a method to show and identify machine intelligent
behavior in different-field interaction with human beings. So,
language communication seems to become both a lowest
common denominator for every field in which it is possible
testing intelligence and, at the same time, a way to cut single
field or domain for testing intelligence from the point of view of
interaction. Now we will consider a few of them, in order to
investigate and discuss whether they could be relevant for qualia
problem.

3

A similar way of thinking seems to be suggested, as regards
specifically natural language, by an old mental experiment formulated
by Putnam, in which he imagines a human being learning by heart a
passage in a language he did not know and then repeating it in a sort of
stream of consciousness. If a telepath, knowing that particular
language, could perceive the stream of consciousness of the human
being who has memorized the passage, the telepath could think the
human being knows that language, even though it is not so. What does
it lack in the scene described in the mental experiment? A real
interaction. As a matter of fact, the conclusion of Putnam himself is
that: «the understanding, then, does not reside in the words themselves,
nor even in the appropriateness of the whole sequence of words and
sentences. It lies, rather, in the fact that an understanding speaker can
do things with the words and sentences he utters (or thinks in his head)
besides just utter them. He can answer questions, for example […].»
[19]. And it appears to be very close to what Turing thought more than
twenty years before.
4
For example, consider the target to build a General Problem Solver
pursued by Newell, Shaw and Simon for long [15, 16].

2 LANGUAGE TRANSLATION AS
CULTURAL INTERACTION
A first field in which language and interaction are involved is
language translation. We know that machine translation is a very
difficult target of computer science and AI since their origins up
to nowadays. The reason is that translation usually concerns two
different natural languages, two tongues, and it is not a merely
act of substitution. On the contrary, translation involves many
different levels of language: syntactic and semantic levels, but
also cultural and stylistic levels, that are very context-dependent.
It is very difficult for a machine to find the correct word or
expression to yield in a specific language what is said in another
language. Many different approaches in this field, especially
from computational linguistic, are available to solve the problem
of a good translation. But anyway, it is an operation that still
remains improvable. As a matter of fact, if we consider some
machine translation tools like Google Translator, there are
generally syntactic and semantic problems in every product of
such tools, even if, maybe, the latter are larger than the former.
So, how can we test intelligence in this field concerning
language? Or, in other terms, what could be a real test for
detecting intelligence as regards translation? A tool improvement
could be not satisfying. We could think indeed that, with the
improvement of machine translation tools, we could have better
and better outcomes in this field, but what we want is not a
collection of excellent texts, from the point of view of
translation. What we want is a sort of justification of the word
choice in the empirical activity of translation. If we could have a
program that is able to justify its choosing of words and
expressions in the act of translation, we could consider that the
problem of a random good choice of a word or of an expression
is evaded.
In a dialogue, a personal tribute to Alan Turing, Douglas
Hofstadter underlines a similar view. Inspired by the two little
snippets of Turing’s 1950 article [26], Hofstadter builds a
(fictitious) conversation between a human being and a machine
in order to show the falsity of simplistic interpretations of Turing
Test, that he summarizes in the following way: «even if some AI
program passed the full Turing Test, it might still be nothing but
a patchwork of simple-minded tricks, as lacking in
understanding or semantics as is a cash register or an
automobile transmission» [10]. In his dialogue, Hofstadter tries
to expand the flavor of the second Turing snippet, where Mr
Pickwick is compared to a winter’s day [26]. The conversation
by Hofstadter has translation as the main topic, in particular
poetry translation. Hofstadter wants to show how complex such
a subject is and that it is very difficult that a program could have
a conversation of that type with a human being, and thus pass the
Turing Test. By reversing perspective, we can consider
translation one of the language field in which, in the future, it
could be fruitful testing machine intelligence. But we are not
merely referring to machine translation. We want to suggest the
a conversation on a translation subject could be a target for a
machine. Translation by itself, indeed, concerns many cultural
aspects, as we said before, and the understanding and
justification of what term or expression is suitable in a specific
context of a specific language could be a very interesting
challenge for a program, that would imply the knowledge of the
cultural context of a specific language by the program, and

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35

therefore the implementation of mechanisms for representing
and handling two different language contexts.
In Hofstadter’s dialogue, much attention is devoted to the
problem from a poetic point of view. We can have a flavour of
the general issues involved by considering an extract from the
dialogue, which is between two entities, a Dull Rigid Human and
an Ace Mechanical Translator:
«DRH: Well, of course, being an advanced AI program, you
engaged in a highly optimized heuristic search.
AMT: For want of a better term, I suppose you could put it
that way. The constraints I found myself under in my search
were, of course, both semantic and phonetic. Semantically, the
problem was to find some phrase whose evoked imagery was
sufficiently close to, or at least reminiscent of, the imagery
evoked by croupir dans ton lit. Phonetically, the problem was a
little trickier to explain. Since the line just above ended with
“stir”, I needed an “ur” sound at the end of line 6. But I didn’t
want to abandon the idea of hyphenating right at that point. This
meant that I needed two lines that matched this template:
Instead of …ur…ing …… bed
where the first two ellipses stand for consonants (or
consonant clusters), and the third one for “in” or “in your” or
something of the sort. Thus, I was seeking gerunds like
“lurking”, “working”, “hurting”, “flirting”, “curbing”,
“squirming”, “bursting”, and so on — actually, a rather rich
space of phonetic possibilities.
DRH: Surely you must have, within your vast data bases, a
thorough and accurate hyphenation routine, and so you must
have known that the hyphenations you propose — “lur-king”,
“squir-ming”, “bur-sting”, and so forth — are all illegal…
AMT: I wish you would not refer to my knowledge as “your
vast data bases”. I mean, why should that quaint, old-fashioned
term apply to me any more than to you? But leaving that quibble
aside, yes, of course, I knew that, strictly speaking, such
hyphenations violate the official syllable boundaries in the eyes
of rigid language mavens like that old fogey William Safire. But
I said to myself, “Hey, if you’re going to be so sassy as to
hyphenate a word across a line-break, then why not go whole
hog and hyphenate in a sassy spot inside the word?”» [10].
Poetry involves metrical structures, rhymes, assonances,
alliterations and many other figures of speech [10]. But, they
constitute some constraints that are easily mechanizable, by
means of the appropriate set of data bases. In fact, a machine
could be faster than a human being in finding, for example,
every word rhyming with a given one. So the problem is not if
we have to consider poetry or prose translation, and their
differences, but that of catching the cultural and personal flavor
of the text’s author, within a figure of speech scheme or not.
Poetry just has some further, but mechanizable, constraints. So,
what remains outside such constraints? Is it the traditional idea
of an intentionality of terms? We do not think that things are
those. The notion of intentionality seems always to involve a
first-person, subjective point of view that is undetectable in a
machine, as a long debate of last thirty years seems to show. But
if we consider the natural development of intentionality problem,
that of qualia, (as subjective conscious experiences that we are
able to express with words), maybe we could have a better

problem and find a better field of investigation in considering
translation as a sort of qualia communication. In other terms, a
good terminological choice and a good justification of such a
choice could be a suitable method for testing intelligence, even
in its capability to express and understand qualia. And this could
be a consequence of the fact that, generally speaking, translation
is a sort of communication, a communication of contents from a
particular language to another particular language; and in the end
a context interaction.

3 INTERACTION BETWEEN MODEL AND
REALITY
Another field in which the notion of interaction could be relevant
from the point of view of the Turing Test is that of scientific
discovery. In the long development of machine learning some
researchers implemented programs that are able to carry out
generalizations from data structures within a specific scientific
domain, namely scientific laws5. Even thought they are very
specific laws, they are (scientific) laws in all respects. Such
programs were based on logic method and, indeed, they could
only arrive to a generalization from data structures and they were
not able to obtain their outcomes from experimental conditions.
More recently, other artificial artifacts have been built in order to
fill such a gap. For example, ADAM [8] is a robot programmed
for carrying out outcomes in genetics with the possibility of
autonomously managing real experiments. It has a logic-based
knowledge base that is a model of metabolism, but it is able as
well to plan and run experiments to confirm or disconfirm some
hypotheses within a research task. In particular, it could set up
experimental conditions and situations with a high level of
resource optimization for investigating gene expression and
associating one or more genes to one protein. The outcome is a
(very specific but real) scientific law, or a set of them. We could
say that ADAM is a theoretical and practical machine. It
formulates a number of hypotheses of gene expression using its
knowledge bases, that includes all that we already know about
gene expression from a biological point of view. It does the
experiments to confirm or disconfirm every hypothesis, and then
it carries out a statistical analysis for evaluating the results. So, is
ADAM a perfect scientist, an autonomous intelligent artifacts in
the domain of science?

Figure 1. Diagram of the hypotheses generation–experimentation cycle
for the production of new scientific knowledge, on which ADAM is
based (from [21]).

5

For example GOLEM. For some outcomes of it, see [14]; for a
discussion see [5].

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36

Of course, it is true that its outcomes are original in some
cases; and it is also true that its creators, its programmers do not
see in it a substitute for scientists, but only an assistant for
human scientists, even though a very efficient one, at least at the
current phase of research, likewise it happens in other fields like
chess playing and music. What does lack ADAM to become a
scientist in? We could say that it lacks in the possibility of
controlling or verifying its outcomes from different points of
view, for example from an interdisciplinary perspective. But it
seems a mere practical limit, surmountable with a lot of
additional scientific knowledge of different domains, given that
it has the concrete possibility to do experiments. Yet, as regards
such a specific aspect, what is the reach of ADAM – or other
programs devoted to scientific discovery, like EVE, specialized
in pharmaceutical field – in conducting experiments? Or, that is
the same thing, how far could it get in formulating hypotheses?
It all seems to depend on its capacity of interaction with the real
world. And so we could say that in order to answer the question
if ADAM or other similar artificial artifacts are intelligent, we
have to consider not only the originality of their outcomes, but
also their creativity in the hypothesis formulation, task that is
strictly dependent on its practical interaction with the real world.
Is this a violation of what Turing said we have not to consider in
order to establish if a machine is intelligent, namely its
“physical” difference from human beings? We think not. We
think that interaction between a model of reality and reality itself
from a scientific point of view is the most important aspect in
scientific discovery and it could be in the future one of the way
in which evaluate the results of artificial artifacts and their
intelligence. As a matter of fact, science and scientific discovery
take place in a domain in which knowledge and methods are
widely structured and the invention of new hypotheses and
theories could reveal itself as a task of combination of previous
knowledge, even expressed in some symbolic language, more
than a creation from nothing. And the capability to operate such
a combination could be the subjective perspective, the first
person point of view of future machines.

4 EMOTION INTERACTING: THE CASE OF
LOVE
Another field in which the notion of interaction could be relevant
from the point of view of Turing Test are emotions, their role in
the interaction with the environment and the language to transmit
the emotions. Emotions are cognitive phenomena. It is not
possible to characterize them as irrational dispositions, but they
provide with all the necessary information about the word
around us. The emotions are a way to relate the environment and
other individuals. Emotions are probably a necessary condition
for our mental life [2, 6]. They show us our radical dependence
on the natural and social environment.
One of the most significant cognitive emotions is love. Since
antiquity, philosophers have considered love as a crucial issue in
their studies. Modern day psychologists have discussed its
dynamics and dysfunctions. However, it has rarely been
investigated as a genuine human cognitive phenomenon. In its
most common sense, love has been considered in poetry,
philosophy, and literature, as being something universal, but at
the same time, as a radically subjective feeling. This ambiguity
is the reason why love is such a complicated subject matter.
Now, we want to argue that love, by means of its rational

character, can be studied in a scientific way. According to the
philosophical tradition, human beings are rational animals.
However, the same rationality guides us in many circumstances,
sometimes creates difficult puzzles. Feelings and emotions, like
love, fortunately are able to offer an efficient reason for action.
Even if what “love” is defies definition, it remains a crucial
experience in the ordinary life of human beings. It participates in
the construction of human nature and in the construction of an
individual’s identity. This is shown by the universality of the
feeling of love across cultures. It is rather complicated to offer a
precise definition of “love”, because its features include
emotional states, such as tenderness, commitment, passion,
desire, jealousy, and sexuality. Love modifies people’s way of
thinking and acting, and it is characterized by a series of physical
symptoms. In fact, love has often been considered as a type of
mental illness. How many kinds of love are there? In what
relation are they?
Over the past decades many classifications of love have been
proposed. Social psychologists such as Berscheid and Walster
[1], for example, in their cognitive theory of emotion, propose
two stages of love. The former has to do with a state of
physiological arousal and it is caused by the presence of positive
emotions, like sexual arousal, satisfaction, and gratification, or
by negative emotions, such as fear, frustration, or being rejected.
The second stage of love is called “tagging”, i.e., the person
defines this particular physiological arousal as a “passion” or
“love”. A different approach is taken by Lee [12] and Hendrick
[7, 9]. Their interest is to identify the many ways we have for
classifying or declining love. They focus their attention on love
styles, identifying six of them: Eros, Ludus, Mania, Pragma,
Storge and Agape. Eros (passionate love) is the passionate love
which gives central importance to the sexual and physical
appearance of the partner; Ludus (game-playing love) is a type
of love exercised as a game that does not lead to a stable, lasting
relationship; Mania (possessive, dependent love) is a very
emotional type of love which is identified with the stereotype of
romantic love; Pragma (logical love) concerns the fact that
lovers have a concrete and pragmatic sense of the relationship,
using romance to satisfy their particular needs and dictating the
terms of them; Storge (friendship-based love) is a style in which
the feeling of love toward each other grows very slowly. Finally,
it is possible to speak of Agape (all-giving selfless love)
characterized by a selfless, spiritual and generous love,
something rarely experienced in the lifetime of individuals.
Robert Sternberg [20] offers a graphical representation of love
called the “triangle theory”. The name stems from the fact that
the identified components are the vertices of a triangle. The work
of the Yale psychologist deviates from previous taxonomies, or
in other words, from the previous attempts made to offer a
catalogue of types of existing love. The psychological elements
identified by Sternberg to decline feelings of love are three:
intimacy, passion, decision/commitment. The different forms of
love that you may encounter in everyday life would result from a
combination of each of these elements or the lack of them.
Again, in the study and analysis of the feeling of love we
encounter a list of types of love: non-love, affection, infatuation,
empty love, romantic love, friendship, love, fatuous love, love
lived.
Philosophers, fleeing from any kind of taxonomy, approach
the feeling of love cautiously, surveying it and perhaps even
fearing it. Love seems to have something in common with the

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37

deepest of mysteries, i.e. the end of life. It leads us to question,
as death does, the reality around us as well as ourselves, in the
hope that something precious and important not pass us by. But
love is also the guardian of an evil secret that is revealed, which
consists in the nonexistence of the love object, in that it is
nothing but a projection of our own desires. Love is, according
to Arthur Schopenhauer, a sequence of actions performed by
those who know perfectly that there is a betrayal in that it does
nothing else but carry out the painful event which life consists
in. Thus, love, too, has its Maya veil, and once torn down, what
remains? What remains is the imperative of the sexual
reproduction of the species instinct.
Human nature has for Harry G. Frankfurt [4] two fundamental
characteristics: rationality and the capacity to love. Reason and
love are the regulatory authorities that guide the choices to be
made, providing the motivation to do what we do and
constraining it by creating a space which circumscribes or
outlines the area in which we can act. On one hand, the ability to
reflect and think about ourselves leads to a sort of paralysis. The
ability to reflect, indeed, offers the tools to achieve our desires,
but at the same time, is often an impediment to their satisfaction,
leading to an inner split. On the other, the ability to love unites
all our fragments, structuring and directing them towards a
definite end. Love, therefore, seems to be involved in integration
processes of personal identity.
In The Origin of species [3] Charles Darwin assigned great
importance to sexual selection, arguing that language, in its
gradual development, was the subject of sexual selection,
recognizing in it features of an adaptation that we could call
unusual (such as intelligence or morality). The dispute that has
followed concerning language and its origins has ignited the
minds of many scholars and fueled the debate about whether
language is innate or is, on the contrary, a product of learning.
Noam Chomsky has vigorously fought this battle against the
tenets of social science supporting that language depends on an
innate genetic ability.
Verbal language is a communication system far more
complex than other modes of communication. There are strong
referential concepts expressed through language that are capable
of building worlds. Similar findings have been the main causes
of the perception of language within the community of scholars,
as something mysterious, something that appeared suddenly in
the course of our history. For a long time arguments concerning
the evolution of language were banned and the idea that a similar
phenomenon could be investigated and argued according to the
processes that drive the evolution of the natural world were
considered to be of no help in understanding the complex nature
of language. Chomsky was one of the main protagonists of this
theoretical trend. According to Chomsky, the complex nature of
language is that it can be understood only through a formal and
abstract approach such as the paradigm of generative grammar.
This theoretical position puts out the possibility of a piecemeal
approach to the study of language and the ability to use the
theory of evolution to get close to understanding it. Steven
Pinker and Paul Bloom, two well-known pupils of Chomsky, in
an article entitled “Natural Language and Natural Selection”,
renewed the debate on the origin of language, stating that it is
precisely the theory of evolution that presents the key to
explaining the complexity of language. A fascinating hypothesis
on language as a biological adaptation is that which considers it
an important feature in courtship. Precisely for this reason it

would have been subject to sexual selection [13]. A good part of
courtship has a verbal nature. Promises, confessions, stories,
statements, requests for appointments are all linguistic
phenomena. In order to woo, find the right words, find the right
tone of voice and the appropriate arguments, you need to employ
language.
Even the young mathematician Alan Turing utilized the
courtship form to create his imitation game with the aim of
finding an answer to a simple – but only in appearance –
question (“can machines think?”). Turing formulated and
proposed a way to establish it by means of a game that has three
protagonists as subject: a man, a woman and an interrogator. The
man and woman are together in one room, in another place is the
interrogator and communication is allowed through the use of a
typewriter. The ultimate goal of the interrogator is to identify if
on the other side there is a man or a woman. The interesting part
concerns what would happen if in the man’s place a computer
was put that could simulate the communicative capabilities of a
human being. As we mentioned before, the thing that Turing
emphasizes in this context is that the only point of contact
between human being and machine communication is language.
If your computer is capable of expressing a wide range of
linguistic behavior appropriate to the specific circumstances it
can be considered intelligent. Among the behaviors to be
exhibited, Turing insert kindness, the use of appropriate words,
and autobiographical information. The importance of
transferring to whoever stands in front of us autobiographical
information, coating therefore the conversation with a personal
and private patina, the expression of shared interests, the use of
kindness and humor, are all ingredients typically found in the
courtship rituals of human beings. It is significant that a way in
which demonstrating the presence of a real human being passed
through a linguistic courtship, a mode of expression that reveals
the complex nature of language and the presence within it of
cognitive abilities. Turing asks: “Can machines think?”, and we
might answer: “Maybe, if they could get a date on a Saturday
evening”.
To conclude, in the case of a very particular phenomenon
such as love, one of the most intangible emotions, Turing shoves
us to consider the role of language as fundamental. But love is a
very concrete emotion as well, because of its first person
perspective. Nevertheless, in order to communicate it also we
human beings are compelled to express it by words in the best
way we can, and at the same time we have just language for
understanding love emotion in other entities (of course, human
beings), together with every real possibility of making mistake
and deceiving ourselves. And so, if we admit the reality of this
emotion also from a high level cognitive point of view, that
involves intelligence and rationality, we have two consequences.
The first one is that just interaction reveals love; the second one
is that just natural language interaction, made of all the complex
concepts that create a bridge between our feelings and the ones
of another human being, reveals the qualia of the entity involved
in a love exchange. Probably that is why Turing wanders through
that subject in his imitation game. And probably the
understanding of this kind of interaction could be, in the future, a
real challenge for artificial artifacts provided with “qualia
detecting sensor”, that cannot be so much different from qualia
itself.

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38

5 A TURING TEST FOR HUMAN (BEING)
BRAIN
A last way in which we could see interaction (connected to
language) as relevant for testing intelligence in machines needs
two perspective reversals. The first one concerns the use of
Turing-Test-like methods to establish the presence of (a certain
level of) consciousness in unresponsive brain damage patients.
As a matter of fact, such patients are not able to use natural
language for communicating as human beings usually do. So
researchers try to find signs of communications that are different
from languages, like blinks of an eyelid, eye-tracking, simple
command following, response to pain, and they try at the same
time to understand if they are intentional or automatic [22]. In
such cases, neurologists are looking for signs of intelligence,
namely of the capability of using intentionally cognitive faculties
through a behavioral method that overturns the one of Turing. In
the case of machines and Turing Test, natural language faculty is
the evidence of the presence of intelligence in machines; in the
case of unresponsive brain damage patients, scientists assume
that patients were able to communicate through natural language
before damage, and so that they were and are intelligent because
intelligence is a human trait. Thus, they look for bodily signs to
establish a communication that is forbidden through usual
means.
This is even more relevant if we consider vegetative state
patient, that are not able to perform any body movement. In the
last years, some researchers supposed that it is possible to
establish a communication with vegetative state patients, a
communication that would show also a certain level of
consciousness, by means of typical neuroimaging techniques,
like fMRI and PET [17]6. In short, through such experiments
they observed that some vegetative state patients, unable to carry
out any body response, had a brain activation very similar to that
of healthy human beings when they were requested with auditory
instructions to imagine themselves walking through one’s house
or playing tennis. Even though the interpretation of such
outcomes is controversial, because of problems regarding
neuroimaging methodology and the nature itself of conscious
activity, if we accept them, they would prove perhaps the
presence of a certain level of consciousness in this kind of
patients, namely the presence of consciousness in mental
activities. They would prove, thus, the presence of intentionality
in the patient response, and not only of cognitive processes or
activities, that could be just cognitive “island” of mental
functioning [11].
Such experimental outcomes could be very useful for building
new techniques and tools of brain-computer interaction for
people who are no longer able to communicate by natural
language and bodily movements, even though there are many
problems that have still to be solved from a theoretical and
epistemological point of view as regards the methodology and
the interpretations of such results [23]. Is it a real
communication? Are those responses a sign of awareness? Could
those responses be real answers to external request?
Yet, what is important for our argumentation is the possibility
of back-transferring these outcomes to machines, and this is the
second reversal we mentioned before. As a matter of fact, these
experiments are based on the assumption that also human beings
6

are machines and that communication is interaction between
mechanical parts, also in the case of subjective, phenomenal
experiences, that are evoked by means of language, but without
external signs. So, the challenging question is: is it possible to
find a parallel in machines? Is it possible to re-create in artificial
artifacts this kind of communication that is not behavioral, but is
still mechanical and detectable inside machines – virtual or
concrete mechanisms – and is simultaneously a sign of
consciousness and awareness in the sense of qualia? Is this sort
of (non-natural-language) communication, if any, a way in
which we could find qualia in programs or robots? Is it the sort
of interaction that could lead us to the feeling of machines?

REFERENCES
[1] E. Berscheid, E. Walster, Interpersonal Attraction, Addison-Wesley,
Boston, Mass., 1978.
[2] A.R. Damasio, Descartes’ Error: Emotion, Reason, and the Human
Brain, Putnam Publishing, New York, 1994.
[3] C. Darwin, On the Origin of Species by Means of Natural Selection,
or the Preservation of Favoured Races in the Struggle for Life,
Murray, London, 1859.
[4] H.G. Frankfurt, The Reasons of Love, Princeton University Press,
Princeton, 2004.
[5] D. Gillies, Artificial Intelligence and Scientific Method, Oxford
University Press, Oxford, 1996.
[6] P. Griffith, What emotions really are. The Problem of Psychological
Categories, Chicago University Press, Chicago, 1997.
[7] C. Hendrick, S. Hendrick, ‘A Theory and a Method of Love’, Journal
of Personality and Social Psychology, 50, 392–402, (1986).
[8] R.D.King, J. Rowland, W. Aubrey, M. Liakata, M. Markham, L.N.
Soldatova, K.E. Whelan, A. Clare, M. Young, A. Sparkes, S.G.
Oliver, P. Pir, ‘The Robot Scientist ADAM’, Computer, 42, 8, 46–54,
(2009).
[9] C. Hendrick, S. Hendrick, Romantic Love, Sage, California, 1992.
[10] D.R. Hofstadter, Le Ton beau de Marot, Basic Books, New York,
1997.
[11] S. Laureys, ‘The neural correlate of (un)awareness: lessons from the
vegetative state’, Trends in Cognitive Sciences, 9, 12, 556–559,
(2005).
[12] J. Lee, The Colors of Love, Prentice-Hall, Englewood Cliffs, 1976.
[13] G.F. Miller, The Mating Mind. How Sexual Choice Shaped the
Evolution of Human Nature, Anchor Books, London, 2001.
[14] S. Muggleton, R.D. King, M.J.E. Sternberg, ‘Protein secondary
structure prediction using logic-based machine learning’, Protein
Engineering, 5, 7, 647–657, (1992).
[15] A. Newell, J.C. Shaw, H.A. Simon, ‘Report on a general problemsolving program’, Proceedings of the International Conference on
Information Processing, pp. 256–264, (1959).
[16] A. Newell, H.A. Simon, Human problem solving, Prentice-Hall,
Englewood Cliffs, NJ, 1972.
[17] A.M. Owen, N.D. Schiff, S. Laureys, ‘The assessment of conscious
awareness in the vegetative state’, in S. Laureys, G. Tononi (eds.),
The Neurology of Consciousness, Elsevier, pp. 163–172, 2009.
[18] A.M. Owen N.D. Schiff, S. Laureys, ‘A new era of coma and
consciousness science’, Progress in Brain Research, 177, 399–411,
(2009).
[19] H. Putnam, Mind, Language and Reality. Philosophical Papers,
Vol. 2. Cambridge University Press, Cambridge, 1975.
[20] R. Sternberg, ‘A Triangular Theory of Love’, Psychological
Review, 93, 119–35, (1986).
[21] A. Sparkes, W. Aubrey, E. Byrne, A. Clare, M.N. Khan, M. Liakata,
M. Markham, J. Rowland, L.N. Soldatova, K.E. Whelan, M. Young,
R.D. King, ‘Towards Robot Scientists for autonomous scientific
discovery’, Automated Experimentation, 2:1, (2010).

For a general presentation and discussion see also [18, 23].

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39

[22] J.F. Stins, ‘Establishing consciousness in non-communicative
patients: A modern day version of the Turing Test’, Consciousness
and Cognition, 18, 1, 187–192, (2009).
[23] J.F. Stins, S. Laureys, ‘Thought translation, tennis and Turing tests
in the vegetative state’, Phenomenology and Cognitive Science, 8,
361–370, (2009).
[24] A.M. Turing, ‘On Computable Numbers, with an Application to the
Entscheidungsproblem’, Proceedings of the London Mathematical
Society, 42, 230–265, (1936); reprinted in: J. Copeland (ed.), The
essential Turing, Oxford University Press, Oxford, pp. 58-90, 2004.
[25] A.M. Turing, ‘Intelligent Machinery’, Internal report of National
Physics Laboratory, 1948 (1948); reprinted in: J. Copeland (ed), The
essential Turing, Oxford University Press, Oxford, pp. 410–432,
2004.
[26] A.M. Turing, ‘Computing Machinery and Intelligence’, Mind, 59,
433–460, (1950).

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40

Could There be a Turing Test for Qualia?
Paul Schweizer1
Abstract. The paper examines the possibility of a Turing test
designed to answer the question of whether a computational
artefact is a genuine subject of conscious experience. Even given
the severe epistemological difficulties surrounding the 'other
minds problem' in philosophy, we nonetheless generally believe
that other human beings are conscious. Hence Turing attempts to
defend his original test (2T) in terms of operational parity with
the evidence at our disposal in the case of attributing
understanding and consciousness to other humans. Following
this same line of reasoning, I argue that the conversation-based
2T is far too weak, and we must scale up to the full linguistic and
robotic standards of the Total Turing Test (3T).
Within this framework, I deploy Block's distinction
between Phenomenal-consciousness and Access-consciousness
to argue that passing the 3T could at most provide a sufficient
condition for concluding that the robot enjoys the latter but not
the former. However, I then propose a variation on the 3T,
adopting Dennett's method of 'heterophenomenology', to
rigorously probe the robot's purported 'inner' qualitative
experiences. If the robot could pass such a prolonged and
intensive Qualia 3T (Q3T), then the purely behavioural evidence
would seem to attain genuine parity with the human case.
Although success at the Q3T would not supply definitive proof
that the robot was genuinely a subject of Phenomenalconsciousness, given that the external evidence is now
equivalent with the human case, apparently the only grounds for
denying qualia would be appeal to difference of internal
structure, either physical-physiological or functionalcomputational. In turn, both of these avenues are briefly
examined. 1the

1

INTRODUCTION

which underpins cognitive science, Strong AI and various allied
positions in the philosophy of mind, computation (of one sort or
another) is held to provide the scientific key to explaining
mentality in general and, ultimately, to reproducing it artificially.
The paradigm maintains that cognitive processes are essentially
computational processes, and hence that intelligence in the
natural world arises when a material system implements the
appropriate kind of computational formalism. So this broadly
Computational Theory of Mind (CTM) holds that the mental
states, properties and contents sustained by human beings are
fundamentally computational in nature, and that computation, at
least in principle, opens the possibility of creating artificial
minds with comparable states, properties and contents.

1

Institute for Language, Cognition and Computation, School of
Informatics, Univ. of Edinburgh, EH8 9AD, UK. Email:
!"#$%&'()*+)",)#-.

Traditionally there are two basic features that are held
to be essential to minds and which decisively distinguish mental
from non-mental systems. One is representational content:
mental states can be about external objects and states of affairs.
The other is conscious experience: roughly and as a first
approximation, there is something it is like to be a mind, to be a
particular mental subject. As a case in point, there is something it
is like for me to be consciously aware of typing this text into my
desk top computer. Additionally, various states of my mind are
concurrently directed towards a number of different external
objects and states of affairs, such as the letters that appear on my
monitor. In stark contrast, the table supporting my desk top
computer is not a mental system: there are no states of the table
that are properly about anything, and there is nothing it is like to
be the table.
be applied to a system with no representational states, so too,
many would claim that a system entirely devoid of conscious
experience cannot be a mind. Hence if the project of Strong AI
is to be successful at its ultimate goal of producing a system that
truly counts as an artificially engendered locus of mentality, then
it would seem necessary that this computational artefact be fully
conscious in a manner comparable to human beings.

2

CONSCIOUSNESS AND THE ORIGINAL
TURING TEST

In 1950 Turing [1] famously proposed an answer to the question

has since become universally referred to as the 'Turing test' (2T).
In

can pose questions to the remaining two players, where the goal
of the game is for the questioner to determine which of the two
respondents is the computer. If, after a set amount of time, the
questioner guesses correctly, then the machine loses the game,
and if the questioner is wrong then the machine wins. Turing
claimed, as a basic theoretical point, that any machine that could
win the game a suitable number of times has passed the test and
should be judged to be intelligent, in the sense that its behavioral
performance has been demonstrated to be indistinguishable from
that of a human being.
In his prescient and ground breaking article, Turing
explicitly considers the application of his test to the question of
machine consciousness. This is in section (4) of the paper, where
he considers the anticipated 'Argument from Consciousness'
objection to the validity of his proposed standard for answering
the question 'Can a machine think?'. The objection is that, as per
the above, consciousness is a necessary precondition for genuine
thinking and mentality, and that a machine might fool its

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41

interlocutor and pass the purely behavioural 2T, and yet remain
completely devoid of internal conscious experience. Hence
merely passing the 2T does not provide a sufficient condition for
concluding that the system in question possesses the
characteristics required for intelligence and bona fide thinking.
Hence the 2T is inherently defective.
Turing's defensive strategy is to invoke the well known
and severe epistemological difficulties surrounding the very
same question regarding our fellow human beings. This is the
other minds problem
how do you
know that other people actually have a conscious inner life like
only conscious being in the universe. As Turing humorously
notes, this type of 'solipsistic' view (although more accurately
characterized as a form of other minds skepticism, rather than
full blown solipsism), while logically impeccable, tends to make
communication difficult, and rather than continually arguing
over the point, it is usual to simply adopt the polite convention
that everyone is conscious.
Turing notes that on its most extreme construal, the
only way that one could be sure that a machine or another human
being is conscious and hence genuinely thinking is to be the
machine or the human and feel oneself thinking. In other words,
one would have to gain first person access to what it's like to be
the agent in question. And since this is not an empirical option,
conscious all we have to go on is behaviour. Hence Turing
attempts to justify his behavioural test that a machine can think,
and ipso facto, has conscious experience, by claiming parity with
the evidence at our disposal in the case of other humans. He
therefore presents his anticipated objector with the following
dichotomy: either be guilty of an inconsistency by accepting the
behavioural standard in the case of humans but not computers, or
maintain consistency by rejecting it in both cases and embracing
solipsism. He concludes that most consistent proponents of the
argument from consciousness would chose to abandon their
objection and accept his test rather than be forced into the
solipsistic position.
However, it is worth applying some critical scrutiny to
Turing's reasoning at this early juncture. Basically, he seems to
be running epistemological issues together with semantical
and/or factive questions which should properly be kept separate.
mean by saying that a system has a
mind i.e. what essential traits and properties are we ascribing
how we can know that a given system actually satisfies this
behaviouristic methodology has a strong tendency to collapse
these two themes, but it is important to note that they are
conceptually distinct. In the argument from consciousness, the
point is that we mean something substantive, something more
than just verbal stimulus-response patterns, when we attribute
mentality to a system. In this case the claim is that we mean that
the system in question has conscious experience, and this
property is required for any agent to be accurately described with
So one could potentially hold that consciousness is
the term) and that:
(1) other human beings are in fact conscious
(2) the computer is in fact unconscious

though it passes the 2T.
This could be the objective state of affairs that genuinely obtains
in the world, and this is completely independent of whether we
can know, with certainty, that premises (1) and (2) are actually
true. Although epistemological and factive issues are intimately
related and together inform our general practices and goals of
inquiry, nonetheless we could still be correct in our assertion,
without being able to prove
that consciousness was essential to genuine mentality, then one
could seemingly deny that any purely behaviouristic standard
was sufficient to test for whether a system had or was a mind.
In the case of other human beings, we certainly take
behaviour as evidence that they are conscious, but the evidence
could in principle overwhelmingly support a false conclusion, in
both directions. For example, someone could be in a comatose
state where they could show no evidence of being conscious
because they could make no bodily responses. But in itself this
of what was going on and perhaps be able to report,
retrospectively, on past events once out of their coma. And
again, maybe some people really are zombies, or sleepwalkers,
and exhibit all the appropriate external signs of consciousness
oo spell be ruled out a priori.
Historically, there has been disagreement regarding the
proper interpretation of Turing's position regarding the intended
import of his test. Some have claimed that the 2T is proposed as
an operational definition of intelligence, thinking, etc., (e.g.
Block [2], French [3]), and as such it has immediate and
fundamental faults. However, in the current discussion I will
adopt a weaker reading and interpret the test as purporting to
furnish an empirically specifiable criterion for when intelligence
can be legitimately ascribed to an artefact. On this reading, the
main role of behavior is inductive or evidential rather than
constitutive, and so behavioral tests for mentality do not provide
a necessary condition nor a reductive definition. At most, all that
is warranted is a positive ascription of intelligence or mentality,
if the test is adequate and the system passes. In the case of
Turing's 1950 proposal, the adequacy of the test is defended
almost entirely in terms of parity of input/output performance
with human beings, and hence alleges to employ the same
operational standards that we tacitly adopt when ascribing
conscious thought processes to our fellow creatures.
Thus the issue would appear to hinge upon the degree
of evidence a successful 2T performance provides for a positive
conclusion in the case of a computational artefact, (i.e. for the
negation of (2) above), and how this compares to the total body
of evidence that we have in support of our belief in the truth of
(1). We will only be guilty of an inconsistency or employing a
double standard if the two are on a par and we nonetheless
dogmatically still insist on the truth of both (1) and (2). But if it
turns out to be the case that our evidence for (1) is significantly
better than for the negation of (2), then we are not forced into
there is clearly very little parity with the human case. We rely on
far more than simply verbal behaviour in arriving at the polite
convention that other human beings are conscious. In addition to
conversational data, we lean very heavily on their bodily actions
involving perception of the spatial environment, navigation,

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42

physical interaction, verbal and other modes of response to
communally accessible non-verbal stimuli in the shared physical
surroundings, etc. So the purely conversational standards of the
2T are not nearly enough to support a claim of operational parity
with humans. In light of the foregoing observations, in order to
move towards evidential equivalence in terms of observable
behaviour, it is necessary to break out of the closed syntactic
bubble of the 2T and scale up to a full linguistic and robotic
version of the test. But before exploring this vastly strengthened
variation as a potential test for the presence of conscious
experience in computational artefacts, in the next section I will
briefly examine the notion of consciousness itself, since we first
need to attain some clarification regarding the phenomenon in
question, before we go looking for it in robots.

3

TWO TYPES OF CONSCIOUSNESS

Even in the familiar human case, consciousness is a notoriously
elusive phenomenon, and is quite difficult to characterize
rigorously. In addition, the word
uniform and univocal manner, but rather appears to have
different meanings in different contexts of use and across diverse
academic communities. Block [4] provides a potentially
illuminating philosophical analysis of the distinction and
possible relationship between two common uses of the word.
a number of different concepts and denoting a number of
different phenomena. He attempts to clarify the issue by
distinguishing two basic and distinct forms of consciousness that
are often conflated: Phenomenal or P-consciousness and Access
or Ais experience: what makes a state phenomenally conscious is that
controversially, Block holds that P-conscious properties, as such,
he notoriously difficult explanatory gap problem in
philosophical theorizing concerns P-consciousness e.g. how is
it possible that appeal to a physical brain process could explain
what it is like to see something as red?
So we must take care to distinguish this type of purely
qualitative, Phenomenal
consciousness,
from
Access
consciousness, the latter of which Block sees as an information
processing correlate of P-consciousness. A-consciousness states
and structures are those which are directly available for control
of speech, reasoning and action. Hence Block's rendition of Aconsciousness is similar to Baars' [5] notion that conscious
representations are those that are broadcast in a global
workspace. The functional/computational approach holds that
the level of analysis relevant for understanding the mind is one
that allows for multiple realization, so that in principle the same
mental states and phenomena can occur in vastly different types
of physical systems which implement the same abstract
functional or computational structure. As a consequence, a
staunch adherent of the functional-computational approach is
committed to the view that the same conscious states must be
preserved across widely diverse type of physical
implementation. In contrast, a
that details of the particular physical/physiological realization
matter in the case of conscious states. Block says that if P = A,
then the information processing side is right, while if the

biological nature of experience is crucial then we can expect that
P and A will diverge.
A crude difference between the two in terms of overall
characterization is that P-consciousness content is qualitative
while A-consciousness content is representational. A-conscious
states are necessarily transitive or intentionally directed, they are
always states of consciousness of. However. P-conscious states
On Block's account, the paradigm Pconscious states are the qualia associated with sensations, while
the paradigm A-conscious states are propositional attitudes. He
maintains that the A-type is nonetheless a genuine form of
consciousness, and tends to be what people in cognitive
neuroscience have in mind, while philosophers are traditionally
more concerned with qualia and P-consciousness, as in the hard
problem and the explanatory gap. In turn, this difference in
meaning can lead to mutual misunderstanding. In the following
discussion I will examine the consequences of the distinction
between these two types of consciousness on the prospects of a
Turing test for consciousness in artefacts.

4

THE TOTAL TURING TEST

In order to attain operational parity with the evidence at our
command in the case of human beings, a Turing test for even
basic linguistic understanding and intelligence, let alone
conscious experience, must go far beyond Turing's original
proposal. The conversational 2T relies solely on verbal
input/output patterns, and these alone are not sufficient to evince
a correct interpretation of the manipulated strings. Language is
primarily about extra-linguistic entities and states of affairs, and
there is nothing in a cunningly designed program for pure syntax
manipulation which allows it to break free of this closed loop of
symbols and demonstrate a proper correlation between word and
object. When it comes to judging human language users in
normal contexts, we rely on a far richer domain of evidence.
Even when the primary focus of investigation is language
proficiency and comprehension, sheer linguistic input/output
data is not enough. Turing's original test is not a sufficient
condition for concluding that the computer genuinely
understands or refers to anything with the strings of symbols it
f
relations and interactions with the objects and states of affairs in
the real world that its words are supposed to be about. To
illustrate the point; if the computer has no eyes, no hands, no
mouth, and has never seen or eaten anything, then it is not
talking about hamburgers when its program generates the string
-a-m-b-u-r-g-e-rinside a closed loop of syntax.
In sharp contrast, our talk of hamburgers is intimately
connected to nonverbal transactions with the objects of
nonverbal stimuli to appropriate linguistic behaviours. When
given the visual stimulus of being presented with a pizza, a taco
and a kebab, we can produce the salient utterance "Those
particular foodstuffs are not hamburgers". And there are
appropriate nonverbal actions. For example, we can follow
complex verbal instructions and produce the indicated patterns
of behaviour, such as finding the nearest Burger King on the
basis of a description of its location in spoken English. Mastery
of both of these types of rules is essential for deeming that a

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43

human agent understands natural language and is using
expressions in a correct and referential manner - and the hapless
2T computer lacks both.2 2the
And when it comes to testing for conscious experience,
we again need these basic additional dimensions of perception
and action in the real world as an essential precondition. The
fundamental limitations of mere conversational performance
naturally suggest a strengthening of the 2T, later named the Total
Turing Test (3T) by Harnad [7], wherein the repertoire of
relevant behaviour is expanded to include the full range of
intelligent human activities. This will require that the
computational procedures respond to and control not simply a
teletype system for written inputs and outputs, but rather a well
crafted artificial body. Thus in the 3T the scrutinized artefact is a
robot, and the data to be tested coincide with the full spectrum of
behaviours of which human beings are normally capable. In
order to succeed, the 3T candidate must be able to do, in the real
world of objects and people, everything that intelligent people
can do. Thus Harnad expresses a widely held view when he
claims that the 3T is "...no less (nor more) exacting a test of
having a mind than the means we already use with one another...
[and, echoing Turing] there is no stronger test, short of being the
candidate". And, as noted above, the latter state of affairs is not
an empirical option. examined.3 3the
Since the 3T requires the ability to perceive and act in
the real world, and since A-consciousness states and structures
are those which are directly available for control of speech,
reasoning and action, it would seem to follow that the successful
3T robot must be A-conscious. For example, in order to pass the
test, the robot would have to behave in an appropriate manner in
any number of different scenarios such as the following. The
robot is handed a silver platter on which a banana, a boiled egg,
a teapot and a hamburger are laid out. The robot is asked to pick
up the piece of fruit and throw it out the window. Clearly the
robot could not perform the indicated action unless it had direct
information processing access to the identity of the salient
object, its spatial location, the movements of its own mechanical
arm, the location and geometrical properties of the window, etc.
Such transitive, intentionally directed A-conscious states are
plainly required for the robot to pass the test.
But does it follow that the successful 3T robot is Pconscious? It seems, not, since on the face of it there appears to
be no reason why the robot could not pass the test relying on Aconsciousness alone. All that is being tested is its executive
control of the cognitive processes enabling it to reason correctly
and perform appropriate verbal and bodily actions in response to
a myriad of linguistic and perceptual inputs. These abilities are
demonstrated solely through its external behaviour, and so far,
there seems to be no reason for P-conscious states to be invoked.
intelligence and linguistic understanding in the actual world, the
2

Shieber [6] provides a valiant and intriguing rehabilitation/defense of
the 2T, but it nonetheless still neglects crucial data, such as mastery of
language exit and entry rules. Ultimately Shieber's rehabilitation in terms
of interactive proof requires acceptance of the notion that
conversational input/response patters alone are sufficient, which
premise I would deny for the reasons given. The program is still
operating within a closed syntactic bubble.
3
See Schweizer [8] for an argument to the effect that even the combined
linguistic and robotic 3T is still too weak as a definitive behavioural
test of artificial intelligence.

A-conscious robot could conceivably pass the 3T while at the
same time there is nothing it is like to be the 3T robot passing the
test. We are now bordering on issues involved in demarcating
the 'easy' from the 'hard' problems of consciousness, which, if
pursued at this point, would be moving in a direction not
immediately relevant to the topic at hand. So rather than
exploring arguments relating to this deeper theme, I will simply
contend that passing the 3T provides a sufficient condition for
Block's version of A-consciousness, but not for P-consciousness,
since it could presumably be passed by an artefact devoid of
qualia.
Many critics of Block's basic type of view (including
Searle [9] and Burge [10]) argue that if there can be such
-conscious but not P-conscious,
then they are not genuinely conscious at all. Instead, Aand is a form of consciousness only to the extent that it is
parasitic upon P-conscious states. So we could potentially have a
3T for A-consciousness, but then the pivotal question arises, is
A-consciousness without associated qualitative presentations
really a form of consciousness? Again, I will not delve into this
deeper and controversial issue in the present discussion, but
simply maintain that the successful 3T robot does at least exhibit
the type of A-awareness that people in, e.g., cognitive
neuroscience tend to call consciousness. But as stated earlier,
'consciousness' is a multifaceted term, and there are also good
reasons for not calling mere A-awareness without qualia a fullfledged form of consciousness.
For example, someone who was drugged or talking in
their sleep could conceivably pass the 2T while still
'unconscious', that is A-'conscious' but not P-conscious. And a
human sleep walker might even be able to pass the verbal and
robotic 3T while 'unconscious' (again A-'conscious' but not Pconscious). What this seems to indicate is that only A'consciousness' can be positively ascertained by behaviour. But
there is an element of definitiveness here, since it seems
plausible to say that an agent could not pass the 3T without
being A-'conscious', at least in the minimal sense of Aawareness. If the robot were warned 'mind the banana peel' and it
was not A-aware of the treacherous object in question on the
ground before it, emitting the frequencies of electromagnetic
radiation appropriate for 'banana-yellow', then it would not
deliberately step over the object, but rather would slip and fall
and fail the test.

5

A TOTAL TURING TEST FOR QUALIA

In the remainder of the paper I will not pursue the controversial
issue as to whether associated P-consciousness is a necessary
condition for concluding that the A-awareness of the successful
3T robot is genuinely a form of consciousness at all. Instead, I
will explore an intensification of the standard 3T intended to
prod more rigorously for evidential support of the presence of Pconscious states. This Total Turing Test for qualia (Q3T) is a
more focused scrutiny of the successful 3T robot which
emphasizes rigorous and extended verbal and descriptive
probing into the qualitative aspects of the robot's purported
internal experiences. So the Q3T involves unremitting
questioning and verbal analysis of the robot's qualitative inner
experiences, in reaction to a virtually limitless variety of salient
external stimuli, such as paintings, sunsets, musical

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44

performances, tastes, textures, smells, pleasures and pains,
emotive reactions...
Turing suggests a precursor version of this strategy in
his 1950 discussion of the argument from consciousness, where
he observes that the question of machine consciousness could be
addressed by a sustained viva voce, where the artefact was asked
questions directly concerning its aesthetic and other types of
qualitative reactions and judgement in response to opened-ended
questioning by the interrogator. Turing provides a conjectural
illustration of the method in the guise of a 'sonnet writing'
programme being quizzed by a human judge.
Interrogator: In the first line of your sonnet which
reads "Shall I compare thee to a summer's day," would
not "a spring day" do as well or better?
Witness: It wouldn't scan.
Interrogator: How about "a winter's day," that would
scan all right.
Witness: Yes, but nobody wants to be compared to a
winter's day.
Interrogator: Would you say Mr. Pickwick reminded
you of Christmas?
Witness: In a way.
Interrogator: Yet Christmas is a winter's day, and I do
not think Mr. Pickwick would mind the comparison.
Witness: I don't think you're serious. By a winter's day
one means a typical winter's day, rather than a special
one like Christmas.
And so on....
The above sample dialogue serves as a good reminder of just
how difficult the original 2T really is (and consequently why it
hasn't yet been passed). However, this conjectured scenario is
still confined to a merely conversational setting of verbal inputs
and verbal outputs, and hence falls far short of the behavioural
potential of a full 3T edition, as well as the concomitant
evidential standards applicable in the human case.
Plebe and Perconti [11] put forward a strengthened
adaptation of a 2T-style viva voce, where the pivotal difference
is that, in addition to merely linguistic inputs, the computer must
now give appropriate and testable conversational reactions to
uploaded images. This is an interesting and important
augmentation of the original 2T, since the inputs are no longer
strictly linguistic, and the test is aimed at evaluating verbally
plausible responses to stimuli that, to us at least, have a
phenomenal aspect. As an example of the method, Plebe and
Perconti supply an excerpt from a hypothetical conversation.
Interrogator: Do you want to look at a picture of me?
Machine: Oh yes, thanks, let's upload that.
<.... uploading>

Machine: Mmmh, I see several people here, who are
you?
Interrogator: Try to guess.
Machine: Well, I know you are blond and shy, so I
would guess the second from the left.
This appears to be an order of magnitude jump over
the purely verbal 2T, and accordingly its standards of
satisfaction are even more futuristic. However, in terms of the
ultimate goal of providing a test, the passing of which constitutes
a sufficient condition for the presence of genuine conscious
experience in an artefact, it should be noted that the inputs, at a
crucial level of analysis, remain purely syntactic and nonqualitative, in that the uploaded image must take the form of a
digital file. Hence this could at most provide evidence of some
sort of (proto) A-awareness in terms of salient data extraction
and attendant linguistic conversion from a digital source, where
the phenomenal aspects produced in humans by the original (predigitalized) image are systematically corroborated by the
computer's linguistic outputs when responding to the inputted
code.
Although a major step forward in terms of expanding
the input repertoire under investigation, as well as possessing the
virtue of being closer to the limits of practicality in the nearer
term future, this proposed new qualia 2T still falls short of the
full linguistic and robotic Q3T. In particular it tests, in a
relatively limited manner, only one sensory modality, and in
principle there is no reason why this method of scrutiny should
be restricted to the intake of photographic images represented in
digital form. Hence a natural progression would be to test a
computer on uploaded audio files as well. However, this
expanded 2T format is still essentially passive in nature, where
the neat and tidy uploaded files are hand fed into the computer
by the human interrogator, and the outputs are confined to mere
verbal response. Active perception of and reaction to distal
objects in the real world arena are critically absent from this test,
and so it fails to provide anything like evidential parity with the
human case. And given the fact that the selected non-linguistic
inputs take the form of digitalized representations of possible
visual (and/or auditory) stimuli, there is still no reason to think
that there is anything it is like to be the 2T computer processing
the uploaded encoding of an image of, say, a vivid red rose.
But elevated to a full 3T arena of shared external
stimuli and attendant discussion and analysis, the positive
evidence of a victorious computational artefact would become
exceptionally strong indeed. So the extended Q3T is based on a
methodology akin to Dennett's [12] 'heterophenomenology' given the robot's presumed success at the standard Total Turing
Test, we count this as behavioural evidence sufficient to warrant
the application of the intentional stance, wherein the robot is
treated as a rational agent harbouring beliefs, desires and various
other mental states exhibiting intentionality, and who's actions
can be explained and predicted on the basis of the content of
these states. Accordingly, the robot's salient sonic emissions are
interpreted as natural language utterances asserting various
propositions and expressing assorted contents. For the reasons
delineated above in section 4, I would argue that this interpretive
step and application of the intentional stance to a mere artefact is
not evidentially warranted in a limited 2T type of setting, and

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45

hence in the latter case, the syntactic tokens emitted by the
computer cannot be given the semantic value that we would
attribute to utterances produced by our fellow human beings.
However, following Dennett, the gathering of
scientifically rigorous empirical evidence proceeds with
neutrality regarding the possible conscious experience of the
robot. In this extended Q3T context, the artefact is queried not
about uploaded images as 'qualitative' input data, but rather
about its own subjective reactions and attendant introspective
states. The robot is not hand fed selected digital files, but rather
is presented with the same external stimuli as its human
interlocutor, the latter of whom can then interrogate the robot
regarding its purported qualitative experiences in response to the
shared stimuli. Hence the human and the robot can both observe
the same Dürer and the same Dali paintings, listen to the same
Vivaldi and Schubert concertos, attend the same wine tasting
event, eat the same brand of hamburger... And the robot must
discourse on its subjective impressions and reactions like a true
aesthete, and where this discourse can be calibrated by the
qualitative reactions.
As an example of the method, an excerpt from a
hypothetical Q3T interaction might look something like the
following.
Interrogator: Isn't the sunset over the Pacific
magnificent?
Robot: Yes, the colours are sublime. Let's go to the top
of that rise for a better view...
Excellent, now we have the full panorama.
Interrogator: What impression does it make on you?
Robot: As the glowing orange orb of the sun begins to
touch the calm blue line of the horizon, yet another day
is passing. Now the radiant disc begins to sink into the
sea and the background crimson intensifies and grows
warmer. It gives me a feeling of sadness, but at the
same time a glimpse of ineffable beauty...
Ouch!! Why the hell did you pinch me?
Interrogator: Just wanted to bring you back down to
earth - you were starting to wax a bit too lyrical.
Robot: Thanks very much for your concern. Could you
please pass me the bottle of delicious chilled
Chardonnay - I want to savour another glass along
with the last rays of the setting sun.
Interrogator: Here you go.
Robot: Arrrgh, that tastes disgusting! - what happened
to the wine?
Interrogator: Uhh, I just stirred in a little marmite
when you weren't looking - wanted to see how you'd
react. This is a Q3T, after all...
Even though a merely A-conscious robot could conceivably pass
the verbal and robotic 3T while at the same time as there being
nothing it is like for the robot passing the test, in this more

focussed version of the 3T the robot would at least have to be
able to go on at endless length talking about what it's like. And
this talk must be in response to an open ended range of different
combinations of sensory inputs, which are shared and monitored
by the human judge. Such a test would be both subtle and
extremely demanding, and it would be nothing short of
remarkable if it could not detect a fake. And presumably a
human sleepwalker who could pass a normal 3T as above would
nonetheless fail this type of penetrating Q3T (or else wake up in
the middle!), and it would be precisely on the grounds of such
failure that we would infer that the human was actually asleep
and not genuinely P-conscious of what was going on.
If sufficiently rigorous and extended, this would
provide extremely powerful inductive evidence, and indeed to
pass the Q3T the robot would have to attain full evidential parity
with the human case, in terms of externally manifested
behaviour.

6

BEYOND BEHAVIOUR

So on what grounds might one consistently deny qualitative
states and P-consciousness in the case of the successful Q3T
robot and yet grant it in the case of a behaviourally
indistinguishable human? The two most plausible considerations
that suggest themselves are both based on an appeal to essential
differences of internal structure, either physical/physiological or
functional/computational. Concerning the latter case, many
versions of CTM focus solely on the functional analysis of
propositional attitude states such as belief and desire, and simply
ignore other aspects of the mind, most notably consciousness
and qualitative experience. However others, such as Lycan [13],
try to extend the reach of Strong AI and the computational
paradigm, and contend that conscious states arise via the
implementation of the appropriate computational formalism. Let
us denote this extension of the basic CTM framework to the
version of CTM+ might hold that qualitative experiences arise in
virtue of the particular functional and information processing
structure of the human brand of cognitive architecture, and hence
that, even though the robot is indistinguishable in terms of
input/output profiles, nonetheless its internal processing structure
is sufficiently different from ours to block the inference to Pconsciousness. So the non-identity of abstract functional or
computational structure might be taken to undermine the claim
that bare behavioural equivalence provides a sufficient condition
for the presence of internal conscious phenomena.
At this juncture, the proponent of artificial
]
consciousness might appeal to a version of Van Gul
objections. When aimed against functionalism, the missing
qualia arguments generally assume a deviant realization of the
very same abstract computational procedures underlying human
ours in all respects, and the position being supported is that
consciousness is to be equated with states of the biological brain,
rather than with any arbitrary physical state playing the same
functional role as a conscious brain process. For example, in
Block's [15] well known 'Chinese Nation' scenario, we are asked
to imagine a case where each person in China plays the role of a
neuron in the human brain and for some (rather brief) span of
time the entire nation cooperates to implement the same

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46

computational procedures as a conscious human brain. The
rather compelling 'common sense' conclusion is that even though
the entire Chinese population may implement the same
computational structure as a conscious brain, there are
nonetheless no purely qualitative conscious states in this
scenario outside the conscious Chinese individuals involved.
And this is then taken as a counterexample to purely
functionalist theories of consciousness.
-strategy is to claim
that the missing qualia argument begs the question at issue. How
do we know, a priori, that the very same functional role could be
played by arbitrary physical states that were unconscious? The
anti-functionalist seems to beg the question by assuming that
such deviant realizations are possible in the first place. At this
point, the burden of proof may then rest on the functionalist to
try and establish that there are in fact functional roles in the
human cognitive system that could only be filled by conscious
processing states. Indeed, this strategy seems more interesting
than the more dogmatic functionalist line that isomorphism of
abstract functional role alone guarantees the consciousness of
any physical state that happens to implement it.
So to pursue this strategy, Van Gulick examines the
psychological roles played by phenomenal states in humans and
identifies various cognitive abilities which seem to require both
conscious and self-conscious awareness e.g. abilities which
involve reflexive and meta-cognitive levels of representation.
These include things like planning a future course of action,
control of plan execution, acquiring new non-habitual task
behaviours These and related features of human psychological
organization seem to require a conscious self-model. In this
manner, conscious experience appears to play a unique
information throughout the brain. In turn, the proponent of
artificial consciousness might plausibly claim that the successful
Q3T robot must possesses analogous processing structures in
order to evince the equivalent behavioural profiles when passing
the test. So even though the processing structure might not be
identical to that of human cognitive architecture, it must
nonetheless have the same basic cognitive abilities as humans in
order to pass the Q3T, and if these processing roles in humans
require phenomenal states, then the robot must enjoy them as
well.
However, it is relevant to note that Van Gulick's
analysis seems to blur Block's distinction between Pconsciousness and A-consciousness, and an obvious rejoinder at
this point would be that all of the above processing roles in both
humans and robots could in principle take place with only the
latter and not the former. Even meta-cognitive and 'conscious'
self models could be accounted for merely in terms of Aawareness. And this brings us back to the same claim as in the
standard 3T scenario - that even the success of the Q3T robot
could conceivably be explained without invoking Pconsciousness per se, and so it still fails as a sufficient condition
for attributing full blown qualia to computational artefacts.

7

MATTER AND CONSCIOUSNESS

Hence functional/computational considerations seem too weak to
ground a positive conclusion, and this naturally leads to the
question of the physical/physiological status of qualia. If even
meta-cognitive and 'conscious' self models in humans could in

principle be accounted for merely in terms of A-awareness, then
how and why do humans have purely qualitative experience?
One possible answer could be that P-conscious states are
essentially physically based phenomena, and hence result from
or supervene upon the particular structure and causal powers of
the actual central nervous system. And this perspective is reenforced by what I would argue (on the following independent
grounds) is the fundamental inability of abstract functional role
to provide an adequate theoretical foundation for qualitative
experience.
Unlike computational formalisms, conscious states are
inherently non-abstract; they are actual, occurrent phenomena
extended in physical time. Given multiple realizability as a
hallmark of the theory, CTM+ is committed to the result that
qualitatively identical conscious states are maintained across
widely different kinds of physical realization. And this is
tantamount to the claim that an actual, substantive and invariant
qualitative phenomenon is preserved over radically diverse real
systems, while at the same time, no internal physical regularities
need to be preserved. But then there is no actual, occurrent factor
which could serve as the causal substrate or supervenience base
for the substantive and invariant phenomenon of internal
conscious experience. The advocate of CTM+ cannot rejoin that
it is formal role which supplies this basis, since formal role is
abstract, and such abstract features can only be instantiated via
actual properties, but they do not have the power to produce
them.
The only (possible) non-abstract effects that
instantiated formalisms are required to preserve must be
specified in terms of their input/output profiles, and thus internal
experiences, qua actual events, are in principle omitted. So (as
I've also been argued elsewhere: see Schweizer [16,17]) it would
appear that the non-abstract, occurrent nature of conscious states
entails that they must depend upon intrinsic properties of the
brain as a proper subsystem of the actual world (on the crucial
assumption of physicalism as one's basic metaphysical stance obviously other choices, such as some variety of dualism, are
theoretical alternatives). It is worth noting that from this it does
not follow that other types of physical subsystem could not share
the relevant intrinsic properties and hence also support conscious
states. It only follows that they would have this power in virtue
of their intrinsic physical properties and not in virtue of being
interpretable as implementing the same abstract computational
procedure.

8

CONCLUSION

We know by direct first person access that the human central
nervous system is capable of sustaining the rich and varied field
of qualitative presentations associated with our normal cognitive
activities. And it certainly seems as if these presentations play a
vital role in our mental lives. However, given the above critical
observation regarding Van Gulick's position, viz., that all of the
salient processing roles in both humans and robots could in
principle take place strictly in terms of A-awareness without Pconsciousness, it seems that P-conscious states are not actually
necessary for explaining observable human behaviour and the
attendant cognitive processes. In this respect, qualia are rendered
functionally epiphenomenal, since purely qualitative states per se
are not strictly required for a functional/computational account
of human mentality. However, this is not to say that they are

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47

physically epiphenomenal as well, since it doesn't thereby follow
that this aspect of physical/physiological structure does not in
fact play a causal role in the particular human implementation of
this functional cognitive architecture. Hence it becomes a purely
contingent truth that humans have associated P-conscious
experience.
And this should not be too surprising a conclusion, on
the view that the human mind is the product of a long course of
exceedingly happenstance biological evolution. On such a view,
perhaps natural selection has simply recruited this available
biological resource to play vital functional roles, which in
principle could have instead been played by P-unconscious but
A-aware states in a different type of realization. And in this case,
P-conscious states in humans are thus a form of 'phenomenal
overkill', and nature has simply been an opportunist in exploiting
biological vehicles that happened to be on hand, to play a role
that could have been played by a more streamlined and less rich
type of state, but where a 'cheaper' alternative was simply not
available at the critical point in time. Evolution and natural
selection are severely curtailed in this respect, since the basic
ingredients and materials available to work with are a result of
random mutation on existing precursor structures present in the
organism(s) in question. And perhaps human computer scientists
and engineers, not limited by what happens to get thrown up by
random genetic mutations, have designed the successful Q3T
robot utilizing a cheaper, artificial alternative to the overly rich
biological structures sustained in humans.
So in the case of the robot, it would remain an open
question whether or not the physical substrate underlying the
artefact's cognitive processes had the requisite causal powers or
intrinsic natural characteristics to sustain P-conscious states.
Mere behavioural evidence on its own would not be sufficient to
adjudicate, and an independent standard or criterion would be
required.4 4So if P-conscious states are thought to be essentially
physically based, for the reasons given above, and if the robot's
Q3T success could in principle be explained through appeal to
mere A-aware stets on their own, then it follows that the nonidentity of the artefact's physical structure would allow one to
consistently extend Turing's polite convention to one's
conspecifics and yet withhold it from the Q3T robot.

Sciences 4: 115-122 (2000).
[4] N. Block, 'On a confusion about a function of consciousness',
Behavioral and Brain Sciences 18, 227-247, (1995).
[5] B. Baars, A Cognitive Theory of Consciousness, Cambridge
University Press, (1988).
[6] S. Shieber, 'The Turing test as interactive proof', Nous 41:33-60
(2007).
[7]
Minds and Machines 1: 43-54, (1991).
[8] P. Schweizer, 'The externalist foundations of a truly total Turing
test', Mind & Machines, DOI 10.1007/s11023-012-9272-4, (2012).
[9] J. Searle, The Rediscovery of the Mind, MIT Press, (1992).
[10] T. Burge, 'Two kinds of consciousness', in N. Block et al. (eds),
The Nature of Consciousness: Philosophical Debates, MIT Press,
(1997).
[11] A. Plebe and P. Perconti, 'Qualia Turing test: Designing a test for
the phenomenal mind', in Proceedings of the First International
Symposium Towards a Comprehensive Intelligence Test (TCIT),
Reconsidering the Turing Test for the 21st Century, 16-19, (2010).
[12] D. Dennett, Consciousness Explained, Back Bay Books, (1992).
[13] W. G., Lycan, Consciousness, MIT Press, (1987).
[14] R. Van Gul
: Are we all
just armadillos? , in Consciousness: Psychological and
Philosophical Essays, M. Davies and G. Humphreys (eds.),
Blackwell, (1993).
[15] N. Block, 'Troubles with functionalism', in C. W. Savage (ed),
Perception and Cognition, University of Minnesota Press, (1978).
[16] P. Schweizer,
Minds and
Machines, 12, 143-144, (2002)
[17] P. Schweizer, 'Physical instantiation and the propositional
attitudes', Cognitive Computation, DOI 10.1007/s12559-0129134-7, (2012).

REFERENCES
Mind 59: 433A. Turing,
460 (1950).
[2] N. Block, 'Psychologism and behaviorism', Philosophical Review
90: 5-43 (1981).
[3] R. French, 'The Turing test: the first 50 years', Trends in Cognitive

[1]

4

This highlights one of the intrinsic limitations of the Turing test
approach to such questions, since the test is designed as an imitation
game, and humans are the ersatz target. Hence the Q3T robot is designed
to behave as if it had subjective, qualitative inner experiences
indistinguishable from those of a human. However, if human qualia are
the products of our particular internal structure (either physicalphysiological or functional-computational), and if the robot is
significantly different in this respect, then the possibility is open that the
robot might be P-conscious and yet fail the test, simply because its
resulting qualitative experiences are significantly different than ours.
And indeed, a possibility in the reverse direction is that the robot might
even pass the test and sustain an entirely different phenomenology, but
where this internal difference is not manifested in its external behaviour.

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48

Jazz and Machine Consciousness:
Towards a New Turing Test
Antonio Chella1 and Riccardo Manzotti2
Abstract. A form of Turing test is proposed and based on the
capability for an agent to produce jazz improvisations at the
same level of an expert jazz musician.
.12

1 INTRODUCTION
The Essay in the style of Douglas Hofstadter [19] related to the
system EMI by David Cope [11] [12], evokes a novel and
different perspective for the Turing test. The main focus of the
test should be creativity instead of linguistic capabilities: can a
computer be so creative to the point that its creations could be
indistinguishable from those of a human being?
According to Sterberg [36], creativity is the ability to produce
something that is new and appropriate. The result of a creative
process is not reducible to some sort of deterministic reasoning.
No creative activity seems to identify a specific chain of activity,
but an emerging holistic result [25].
Therefore, a creative agent should be able to generate novel
artifacts not by following preprogrammed instructions, but on
the contrary by means of a real creative act.
The problem of creativity has been widely debated in the field
of automatic music composition. The previously cited EMI by
David Cope, subject of the Hoftadter essay, produce impressive
results: even for an experienced listener it is difficult to
distinguish musical compositions created by these programs
from those ones created by a human composer. There is no doubt
that these systems capture some main aspects of the creative
process, at least in music.
However, one may wonders if an agent can actually be
creative without being conscious. In this regard, Damasio [14]
suggests a close connection between consciousness and
creativity. Cope himself in his recent book [13] discusses the
relationship between consciousness and creativity. Although he
does not take a clear position on this matter, he seem to favor the
view according to which consciousness is not necessary for
creative process. In facts, Cope asks if a creative agent should
need to be aware of being creating something and if it needs to
experience the results of its own creations.
The argument of consciousness is typically adopted [3] to
support the thesis according to which an artificial agent can
never be conscious and therefore it can never be really creative.
But recently, there has been a growing interest in machine
consciousness [8] [9], i.e., the study of consciousness through
the design and implementation of conscious artificial systems.
This interest is motivated by the belief that this new approach,
based on the construction of conscious artifacts, can shed new
light on the many critical aspects that affect the mainstream

1
2

University of Palermo, Italy, email: antonio.chella@unipa.it
IULM University, Milan, Italy, email: riccardo.manzotti@iulm.it

studies of consciousness from philosophy and neuroscience.
Creativity is just one of these critical issues.
The relationship between consciousness and creativity is
difficult and complex. On the one side some authors claim the
need of awareness of the creative act. On the other side, it is
suspected that many cognitive processes that are necessary for
the creative act may happen in the absence of consciousness.
However it is undeniable that consciousness is closely linked
with the broader unpredictable and less automatic forms of
cognition, like creativity.
In addition, we could distinguish between the mere
production of new combinations and the aware creation of new
content: if the wind would create (like the monkeys on a
keyboard) a melody which is indistinguishable from the “Va
Pensiero” by Giuseppe Verdi, it would be a creative act? Many
authors would debate this argument [15].
In the following, we discuss some of the main features for a
conscious agent like embodiment, situatedness, emotions and the
capability to have conscious experience. These features will be
discussed with reference to musical expression, and in particular
to a specific form of creative musical expression, namely jazz
improvisation. Musical expression seems to be a form of artistic
expression that most of others is able to immediately produce
conscious experience without filters. Moreover, differently from
olfactory or tactile experiences, musical experience is a kind of
structured experience.
According to Johnson-Laird [20], jazz improvisation is a
specific form of expertise of great interest for the study of the
mind. Furthermore, jazz is a particularly interesting case of study
in relation to creativity. Creativity in a jazz musician is very
different from typical models of creativity. In fact, the creativity
process is often studied with regards to the production of new
abstract ideas, as for example the creation of a new mathematical
theory after weeks of great concentration. On the contrary, jazz
improvisation is a form of immediate and continuous lively
creation process which is closely connected with the external
world made up of musical instruments, people, moving bodies,
environments, audience and the other musicians.

2 CREATIVITY
There are at least two aspects of creativity that is worth
distinguishing since the beginning: syntactic and semantic
creativity. The first one is the capability to recombine a set of
symbols according to various styles. In this sense, if we have
enough patience and time, a random generator will create all the
books of the literary world (but without understanding their
meaning). The second aspect is the capability to generate new
meaning that will be then dressed by appropriate symbols. These
two aspects correspond to a good approximation to the
etymological difference between the terms intelligence and

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49

intuition. Intelligence is often defined as the ability to find novel
connections for different entities, but intuition should be able to
do something more, i.e., to bring in something that was
previously unavailable.
In short, the syntactic manipulation of symbols may occur
without consciousness, but creativity does not seem to be
possible without consciousness.
Machine consciousness is not only a technological challenge,
but a novel field of research that has scientific and technological
issues, such as the relationship between information and
meaning, the ability for an autonomous agent to choose its own
goals and objectives, the sense of self for a robot, the capability
to integrate information into a coherent whole, the nature of
experience. Among these issues there is the capability, for an
artificial agent, to create and to experience its own creations.
A common objection to machine consciousness emphasizes
the fact that biological entities may have unique characteristics
that cannot be reproduced in artifacts. If this objection is true,
machine consciousness may not be feasible. However, this
contrast between biological and artificial entities has often been
over exaggerated, especially in relation to the problems of
consciousness. So far, nobody was able to satisfactorily prove
that the biological entities may have characteristics that can not
be reproduced in artificial entities with respect to consciousness.
In fact, at the a meeting on machine consciousness in 2001 at
Cold Spring Harbor Laboratories, the conclusion from Koch [23]
was that no known natural law prevents the existence of
subjective experience in artifacts. On the other hand, living
beings are subject to the laws of physics, and yet are conscious,
able to be creative and to prove experience.
The contrast between classic AI (focused on manipulation of
syntactic symbols) and machine consciousness (open to consider
the semantic and phenomenal aspects of the mind) holds in all
his strength in the case of creativity.
Is artistic improvisation - jazz improvisation in particular - a
conscious process? This is an open question. The musicologist
Gunther Schuller [33] emphasizes the fact that jazz
improvisation affects consciousness at all levels, from the
minimal to the highest one. It is a very particular kind of creative
process.
Jazz improvisation has peculiar features that set it apart from
the traditional classic improvisation [29]: as part of Western
classical music, improvisation is a kind of real time composition
with the same rules and patterns of classic composition. On the
contrary, jazz improvisation is based on a specific set of patterns
and elements. The melody, the rhythm (the swing), the chord
progressions are some of the issues that need to be analyzed and
studied with stylistic and aesthetic criteria different from those of
Western classical music [10].

3 EMBODIMENT
Embodiment does not simply mean that an agent must have a
physical body, but also and above all, that different cognitive
functions are carried out by means of aspects of the body. The
aspect of corporeality seems to be fundamental to the musical
performance and not only for jazz improvisation. In this regard,
Sundberg & Verrillo [38] analyzed the complex feedback that
the body of a player receives during a live performance. In facts,
auditory feedback is not sufficient to explain the characteristics
of a performance. The movement of the hands on the instrument,

the touch and the strength needed for the instrument to play, the
vibrations of the instrument propagated through the fingers of
the player, the vibration of the air perceived by the player’s
body, are all examples of feedback guiding the musician during a
performance. The player receives at least two types of bodily
feedback: through the receptors of the skin and through the
receptors of the tendons and muscles. Todd [39] assumed a third
feedback channel through the vestibular apparatus.
Making music is essentially a body activity [26]. Embodiment
is fundamental to jazz improvisation: can an agent without a
body, such as a software like EMI that runs on a mainframe, be
able to improvise? Apparently not, because it would miss the
bodily feedback channels described above. And, in fact, the
results obtained by EMI in the version Improvisation are modest
and based on ad hoc solutions. The same problem arises for
consciousness: can a software that run on a mainframe be
conscious?
It does not seem that embodiment is a sufficient condition for
consciousness, but it may be a necessary condition. Basically, a
cognitive entity must be embodied in a physical entity. However,
it is necessary to deeply reflect about the concept of
embodiment.
Trivially, a cognitive agent can not exist without a body; even
AI expert systems are embodied in a computer which is a
physical entity. On the other hand it is not enough to have a body
for an agent in order to be not trivially embodied: the Honda
ASIMO robot3, considered the state of the art of today robotic
technology, is an impressive humanoid robot but its
performances are essentially based on a standard controller in
which the behaviors are almost completely and carefully defined
in advance by its designers.
In addition, biology gives us many examples of animals, such
as the cockroaches, whose morphology is complex and that
allows them to survive without cognitive abilities.
The notion of embodiment is therefore much more deep and
complex than we usually think. Not only the fact that an agent
might have a body equipped with sophisticated sensors and
actuators, but other conditions must be met. The concept of
embodiment requires the ability to appreciate and process the
different feedback from the body, just like an artist during a
musical live performance.

4 SITUATEDNESS
In addition to having a body, an agent is part of an environment,
i.e., it is situated. An artist, during a jam session, is typically
situated in a group where she has a continuous exchange of
information. The artist receives and provides continuous
feedback with the other players of the group, and sometimes
even with the audience, in the case of live performances.
The classical view, often theorized in textbooks of jazz
improvisation [10], suggests that during a session, the player
follows his own musical path largely made up by a suitable
musical sequence of previously learned patterns. This is a partial
view of an effective jazz improvisation. Undoubtedly, the
musician has a repertoire of musical patterns, but she is also able
to deviate from its path depending on the feedback she receives
from other musicians or the audience, for example from

3

http://asimo.honda.com

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50

suggestions from the rhythm section or due to signals of
appreciation from the listeners.
Cognitive scientists (see, e.g., [20]) typically model jazz
improvisation processes by means of
Chomsky formal
grammars. This kind of model appears problematic because it
does not explain the complexity of the interaction between the
player, the rest of the group and the audience.
A more accurate model should take into account the main
results from behavior-based robotics [5]. According to this
approach, a musician may use a repertoire of behaviors that are
activated according to the input she receives and according to an
appropriate priority based on her musical sensibility. Interesting
experiments in this direction have been recently described in the
literature. Roboser [27] is an autonomous robot that can move
autonomously in an environment and generate sound events in
real time according to its internal state and to the sensory input it
receives from the environment. EyesWeb [6] is a complex
system that analyzes body movements and gestures with
particular reference to emotional connotations in order to
accordingly generate sound and music in real time and also to
suitably control robots.
Continuator [28] is a system based on a methodology similar
to EMI, but differently from it, is able to learn and communicate
in real time with the musician. For example, the musician
suggests that musical phrases and the system is able to learn the
style of the musician and to continue and complete the sentences
by interacting with the musician.
However, the concept of situated agent, as the concept of
embodiment, is a complex and articulate one. An effective
situated agent should develop a tight integration development
with their surrounding environment so that, like a living being,
its body structure and cognition would be the result of a
continuous and constant interaction with the external
environment.
A true situated agent is an agent that absorbs from its
surroundings, changes according to it and, in turn, it changes the
environment itself. A similar process occurs in the course of jazz
improvisation: the musicians improvise on the basis of their
musical and life experiences accumulated and absorbed over the
years. The improvisation is then based on the interaction and
also, in the case of a jazz group, even of past interactions with
the rest of the group. Improvisation is modified on the basis of
suggestions received from other musicians and audience, and in
turn changes the performances of the other group musicians. A
good jazz improvisation is an activity that requires a deeply
situated agent.

successful performance the player create a tight empathic
relationship between herself and the listeners.
Gabrielsson & Juslin [17] conducted an empirical analysis of
the emotional relationship between a musician and the listeners.
According to this analysis, a song arouses emotions on the basis
of its structure: for example, a sad song is in a minor key, it has a
slow rhythm and the dissonances are frequent, while an exciting
song is fast, strong, with few dissonances.
The emotional intentions of a musician during a live
performance can be felt by the listener with greater or lesser
effectiveness depending on the song itself. The basic emotional
connotations such as the joy or the sadness are easier to transmit,
while more complex connotation such as solemnity are more
difficult to convey. The particular musical instrument employed
has a relevance in the communication of emotions, and of course
the degree of achieved empathy depends on the skill of the
performer. This analysis shows that an agent, to make an
effective performance, must be able to convey emotions and to
have a model (even implicit) of them.
This hypothesis is certainly attractive, but it is unclear how to
translate it into computational terms. So far, many computational
models of emotions have been proposed in the literature. This is
a very prolific field of research for robotics [16].
However, artificial emotions have been primarily studied at
the level of cognitive processes in reinforcement learning
methods.
Attractive and interesting robotic artifacts have been built
able to convey emotions, although it is uncertain whether these
experiments represent effective steps forward in understanding
emotions. For example, the well known robot Kismet [4] is able
to modify some of its external appearance like raising an
eyebrow, grimace, and so on. during its interactions with an user.
These simple external modifications are associates with
emotions. Actually, Kismet has no real model of emotions, but
merely uses a repertoire of rules defined in advance by the
designer: it is the user that naively, interacting with the robot,
ends up with the attribution of emotions to Kismet. On the other
hand, it is the human tendency to anthropomorphize aspects of
its environment. It is easy to see a pair of eyes and a mouth in a
random shape, so it is at the same time easy to ascribe emotions
and intentions to the actions of an agent.
In summary, an agent capable of transmitting emotions during
jazz improvisation must have some effective computational
models for generation and evocation of emotions.

6 EXPERIENCE
5 EMOTIONS
Many scholars consider emotions as a basic element for
consciousness. Damasio [14] believes that emotions form a sort
of proto-consciousness upon which higher forms of
consciousness are developed. In turn, consciousness, according
to this frame of reference, is intimately related with creativity.
The relationships between emotions and music have been
widely analyzed in the literature, suggesting a variety of
computational models describing the main mechanisms
underlying the evocation of emotions while listening to music
[21] [22].
In the case of a live performance as a jazz improvisation, the
link between music and emotions is a deep one: during a

Finally, the more complex problem for consciousness is: how
can a physical system like an agent able to improvise jazz to
produce something similar to our subjective experience? During
a jam session, the sound waves generated by the musical
instruments strike our ears and we experience a sax solo
accompanied by bass, drums and piano. At sunset, our retinas are
struck by rays of light and we have the experience of a
symphony of colors. We swallow molecules of various kinds
and, therefore, we feel the taste of a delicious wine.
It is well known that Galileo Galilei suggested that smells,
tastes, colors and sounds do not exist outside the body of a
conscious subject (the living animal). Thus experience would be
created by the subject in some unknown way.

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A possible hypothesis concerns the separation between the
domain of experience, namely, the subjective content, and the
domain of objective physical events. The claim is that physical
reality can be adequately described only by the quantitative point
of view in a third person perspective while ignoring any
qualitative aspects. After all, in a physics textbook there are
many mathematical equations that describe a purely quantitative
reality. There is room for quality content, feelings or emotions.
Explaining these qualitative contents is the hard problem of
consciousness [7].
Yet scholars as Strawson [37] questioned the validity of such
a distinction as well as the degree of real understanding of the
nature of the physical world.
Whether the mental world is a special construct generated by
some feature of the nervous systems of mammals, is still an open
question. It is fair to stress that there is neither empirical
evidence nor theoretical arguments supporting such a view. In
the lack of a better theory, we could also take into consideration
the idea inspired by externalism [31] [32] according to which the
physical world comprehends also those features that we usually
attribute to the mental domain. A physicalist must be held that if
something is real, and we assume consciousness is real, it has to
be physical. Hence, in principle, a device can envisage it.
In the case of artificial agents for jazz improvisation, how is it
possible to overcome the distinction between function and
experience? Such a typical agent is made up by a set of
interconnected modules, each operating in a certain way. How
the operation of some or all of the interconnected modules
should generate conscious experience? However, the same
question could be transferred to the activity of neurons. Each
neuron, taken alone, does not work differently from a software
module or a chip. But it could remains a possibility: it is not the
problem of the physical world, but of our theories of the physical
world. Artificial agents are part of the same physical world that
produce consciousness in human subjects, so they may exploit
the same properties and characteristics that are relevant for
conscious experience.
In this regard, Tononi [41] proposed a theory supported by
results from neuroscience, according to which the degree of
conscious experience is related to the amount of integrated
information. According to this framework, the primary task of
the brain is to integrate information and, noteworthy, this process
is the same whether it takes place in humans or in artifacts like
agents for jazz improvisation. According to this theory,
conscious experience has two main characteristics. On the one
side, conscious experience is differentiated because the potential
set of different conscious states is huge. On the other side,
conscious experience is integrated; in facts a conscious state is
experienced as a single entity. Therefore, the substrate of
conscious experience must be an integrated entity able to
differentiate among a big set of different states and whose
informational state is greater than the sum of the informational
states of the component sub entities [1] [2].
According to this theory, Koch and Tononi [24] propose a
potential new Turing test based on the integration of
information: artificial systems should be able to mimic the
human being not in language skills (as in the classic version of
Turing test), but rather in the ability to integrate information
from different sources.
Therefore, an artificial agent aware of its jazz improvisation
should be able to integrate during time the information generated

by its own played instrument, the instruments of its band as well
as information from the body, i.e., the feedback from skin
receptors, the receptors of the tendons and muscles and possibly
from the vestibular apparatus. Furthermore, it should also be able
also to integrate information related to emotions.
Some of the early studies based on suitable neural networks
for music generation [40] are promising in the way to implement
an information integration agent. However, we must emphasize
the fact that the implementation of a true information integration
system is a real technological challenge In fact, the typical
engineering techniques for the building of an artifact is
essentially based on the principle of divide et impera, that
involves the design of a complex system through the
decomposition of the system into easier smaller subsystems.
Each subsystem then communicates with the others subsystems
through well-defined interfaces so that the interaction between
the subsystems happen in a very controlled way. Tononi's theory
requires instead maximum interaction between the subsystems in
order to allow an effective integration. Therefore, new
techniques are required to design effective conscious agents.
Information integration theory raised heated debates in the
scientific community. It could represent a first step towards a
theoretically well-founded approach to machine consciousness.
The idea of being able to find the consciousness equations
which, like the Maxwell's equations in physics, are able to
explain consciousness in living beings and in the artifacts, would
be a kind of ultimate goal for scholars of consciousness.

7 CONCLUSIONS
The list of problems related to machine consciousness that have
not been properly treated is long: the sensorimotor experience in
improvisation, the sense of time in musical performance, the
problem of the meaning of a musical phrase, the generation of
musical mental images and so on. These are all issues of great
importance for the creation of a conscious agent for jazz
improvisation, although some of them may overlap in part with
the arguments discussed above.
Although the classic AI achieved impressive results, and the
program EMI by Cope is a great example, so far these issues
have been addressed only partially.
In this article we have discussed the main issues to be
addressed in order to design and build an artificial that can
perform a jazz improvisation. The physicality, the ability to be
located, to have emotions, to have some form of experience are
all problems inherent in the problem of consciousness.
A new Turing test might be based on imitating the ability to
distinguish a jazz improvisation produced by an artificial agent,
maybe able to integrate information according to Tononi, than
improvisation produced by an expert jazz musician.
As should be clear, this is a very broad subject that
significantly extends the traditional the mind-brain problem.
Machine consciousness is, at the same time, a theoretical and
technological challenge that forces to deal with old problems and
new innovative approaches. It is possible, and hope that the
artificial consciousness researchers push to re-examine many
threads left hanging from the Artificial Intelligence and
cognitive science. “Could consciousness be a theoretical time
bomb, ticking away in the belly of AI? Who can say?”
(Haugeland [18], p. 247).

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REFERENCES
[1] D. Balduzzi and G. Tononi, ‘Integrated information in discrete
dynamical systems: Motivation and theoretical framework’, PLoS
Computational Biology, 4, e1000091, (2008).
[2] D. Balduzzi and G. Tononi, ‘Qualia: The geometry of integrated
information’, PLoS Computational Biology, 5, e1000462, (2009).
[3] M. Boden, The Creative Mind: Myths and Mechanisms - Second
Edition, Routledge, London, 2004.
[4] C. Breazeal, Designing Sociable Robots, MIT Press, Cambridge,
MA, 2002.
[5] R. Brooks, Cambrian Intelligence: The Early History of the New
AI, MIT Press, Cambridge, MA, 1999.
[6] Camurri, S. Hashimoto, M. Ricchetti, A. Ricci, K. Suzuki, R.
Trocca and G. Volpe, ‘EyesWeb: Toward Gesture and Affect
Recognition in Interactive Dance and Music Systems’, Computer
Music Journal, 24, 57 – 69, (2000).
[7] D. Chalmers. The Conscious Mind: In Search of a Fundamental
Theory, Oxford University Press, Oxford, 1996.
[8] A. Chella and R. Manzotti (eds.), Artificial Consciousness, Imprint
Academic, Exeter, UK, 2007.
[9] A. Chella and R. Manzotti, ‘Machine Consciousness: A Manifesto
for Robotics’, International Journal of Machine Consciousness, 1,
33 – 51, (2009).
[10] J. Coker, Improvising Jazz, Simon & Schuster, New York, NY,
1964.
[11] D. Cope, ‘Computer Modeling of Musical Intelligence in EMI’,
Computer Music Journal, 16, 69 – 83, 1992.
[12] D. Cope, Virtual Music. MIT Press, Cambridge, MA, 2001.
[13] D. Cope, Computer Models of Musical Creativity, MIT Press,
Cambridge, MA, 2005.
[14] A. Damasio, The Feeling of What Happens: Body and Emotion in
the Making of Consciousness, Houghton Mifflin Harcourt, 1999.
[15] A. Danto, ‘The Transfiguration of Commonplace’, The Journal of
Aesthetics and Art Criticism, 33, 139 – 148, (1974).
[16] J.-M. Fellous and M. A. Arbib, Who Needs Emotions?: The Brain
Meets the Robot, Oxford University Press, Oxford, UK, 2005.
[17] A. Gabrielsson and P.N. Juslin, ‘Emotional Expression in Music
Performance: Between the Performer's Intention and the Listener's
Experience’, Psychology of Music, 24, 68 – 91, (1996).
[18] J. Haugeland, Artificial Intelligence: The Very Idea, MIT Press,
Bradford Books, Cambridge, MA, 1985.
[19] D. Hofstadter, ‘Essay in the Style of Douglas Hofstadter’, AI
Magazine, Fall, 82 – 88, (2009).
[20] P.N. Johnson-Laird, ‘Jazz Improvisation: A Theory at the
Computational Level’, in: Representing Musical Structure, P.
Howell, R. West & I. Cross (eds.), Academic Press, London, 1991.
[21] P. N. Juslin & J. A. Sloboda (eds.), Handbook of Music and
Emotion - Theory, Research, Application, Oxford University Press,
Oxford, UK, 2010.
[22] P.N. Juslin & D. Västfjäll, ‘Emotional responses to music: The
need to consider underlying mechanisms’, Behavioral and Brain
Sciences, 31, 559 – 621, (2008).
[23] K. Koch, ‘Final Report of the Workshop Can a Machine be
Conscious’, The Banbury Center, Cold Spring Harbor Laboratory,
http://theswartzfoundation.com/abstracts/2001_summary.asp (last
access 12/09/2011).
[24] K. Koch and G. Tononi, ‘Can Machines Be Conscious?’, IEEE
Spectrum, June, 47 – 51, (2008).
[25] A. Koestler, The Act of Creation, London, Hutchinson, 1964.
[26] J. W. Krueger, ‘Enacting Musical Experience’, Journal of
Consciousness Studies, 16, 98 – 123, (2009).
[27] J. Manzolli and P.F.M.J. Verschure, ‘Roboser: A Real-World
Composition System’, Computer Music Journal, 29, 55 – 74,
(2005).
[28] F. Pachet, ‘Beyond the Cybernetic Jam Fantasy: The Continuator’,
IEEE Computer Graphics and Applications, January/February, 2 –
6, (2004).

[29] J. Pressing, ‘Improvisation: Methods and Models’, in: Generative
Processes in Music: The Psychology of Performance,
Improvisation, and Composition, J. Sloboda (ed.), Oxford
University Press, Oxford, UK, 1988.
[30] P. Robbins & M. Aydede (eds.), The Cambridge Handbook of
Situated Cognition, Cambridge, Cambridge University Press, 2009.
[31] T. Rockwell, Neither Brain nor Ghost, MIT Press, Cambridge, MA,
2005.
[32] M. Rowlands, Externalism – Putting Mind and World Back
Together Again, McGill-Queen’s University Press, Montreal and
Kingston, 2003.
[33] G. Schuller, ‘Forewords’, in: Improvising Jazz, J. Coker, Simon &
Schuster, New York, NY, 1964.
[34] J. R. Searle, ‘Minds, brains, and programs’, Behavioral and Brain
Sciences, 3, 417 – 457, (1980).
[35] A. Seth, ‘The Strength of Weak Artificial Consciousness’,
International Journal of Machine Consciousness, 1, 71 – 82,
(2009).
[36] R. J. Sternberg (eds.), Handbook of Creativity, Cambridge,
Cambridge University Press, 1999.
[37] G. Strawson, ‘Does physicalism entail panpsychism?’, Journal of
Consciousness Studies, 13, 3 – 31, (2006).
[38] J. Sundberg and R.T. Verrillo, ‘Somatosensory Feedback in
Musical Performance’, (Editorial), Music Perception: An
Interdisciplinary Journal, 9, 277 – 280, (1992).
[39] N.P. McAngus Todd, ‘Vestibular Feedback in Musical
Performance: Response to «Somatosensory Feedback in Musical
Performance»’, Music Perception: An Interdisciplinary Journal,
10, 379 – 382, (1993).
[40] P.M. Todd & D. Gareth Loy (eds.), Music and Connectionism, MIT
Press, Cambridge, MA, 1991.
[41] G. Tononi, ‘An Information Integration Theory of Consciousness’,
BMC Neuroscience, 5, (2004).

AISB/IACAP 2012 Symposium: Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World

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Taking Turing Seriously (But Not Literally)
William York1 and Jerry Swan2
Abstract. Results from present-day instantiations of the Turing test,
most notably the annual Loebner Prize competition, have fueled the
perception that the test is on the verge of being passed. With this perception comes the misleading implication that computers are nearing
human-level intelligence. As currently instantiated, the test encourages an adversarial relationship between contestant and judge. We
suggest that the underlying purpose of Turing’s test would be better served if the prevailing focus on trickery and deception were replaced by an emphasis on transparency and collaborative interaction.
We discuss particular examples from the family of Fluid Concepts architectures, primarily Copycat and Metacat, showing how a modified
version of the Turing test (described here as a “modified Feigenbaum
test”) has served as a useful means for evaluating cognitive-modeling
research and how it can suggest future directions for such work.

1

INTRODUCTION; THE TURING TEST IN
LETTER AND SPIRIT
The method of “postulating” what we want has many advantages; they are the same as the advantages of theft over honest
toil. – Bertrand Russell, Introduction to Mathematical Philosophy
Interrogator: Yet Christmas is a Winter’s day, and I do not
think Mr. Pickwick would mind the comparison.
Respondent: LOL – Pace Alan Turing, “Computing Machinery and Intelligence”

If Alan Turing were alive today, what would he think about the
Turing test? Would he still consider his imitation game to be an effective means of gauging machine intelligence, given what we now
know about the Eliza effect, chatbots, and the increasingly vacuous
nature of interpersonal communication in the age of texting and instant messaging?
One can only speculate, but we suspect he would find current instantiations of his eponymous test, most notably the annual Loebner
Prize competition, to be disappointingly literal-minded. Before going further, it will help to recall Turing’s famous prediction about the
test from 1950:
I believe that in about fifty years’ time it will be possible, to
programme computers, with a storage capacity of about 109 , to
make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making
the right identification after five minutes of questioning ([22],
p. 442).
1
2

Indiana University, United States, email: wwyork@indiana.edu
University of Stirling, Scotland, email: jsw@cs.stir.ac.uk

The Loebner Prize competition adheres closely to the outward
form—or letter—of this imitation game, right down to the fiveminute interaction period and (at least for the ultimate Grand Prize)
the 70-percent threshold.3 However, it is questionable how faithful the competition is to the underlying purpose—or spirit—of the
game, which is, after all, to assess whether a given program or artifact should be deemed intelligent, at least relative to human beings.4
More generally, we might say that the broader purpose of the test is
to assess progress in AI, or at least that subset of AI that is concerned
with modeling human intelligence. Alas, this purpose gets obscured
when the emphasis turns from pursuing this long-term goal to simply “beating the test.” Perhaps this shift in emphasis is an inevitable
consequence of using a behavioral test: “If we don’t want that,” one
might argue, “then let us have another test.” Indeed, suggestions have
been offered for modifying the Turing test (cf. [6], [7], [3]), but we
still see value in the basic idea behind the test—that of using observable “behavior” to infer underlying mechanisms and processes.

1.1 Priorities and payoffs
The letter–spirit distinction comes down to a question of research priorities, of short-term versus long-term payoffs. In the short term, the
emphasis on beating the test has brought programs close to “passing
the Turing test” in its Loebner Prize instantiation. Brian Christian,
who participated in the 2009 competition as a confederate (i.e., one
of the humans the contestant programs are judged against) and described the experience in his recent book The Most Human Human,
admitted to a sense of urgency upon learning that “at the 2008 contest..., the top program came up shy of [passing] by just a single vote”
([1], p. 4). Yet in delving deeper into the subject, Christian realized
the superficiality—the (near) triumph of “pure technique”—that was
responsible for much of this success.
But it is not clear that the Loebner Prize has steered researchers
toward any sizable long-term payoffs in understanding human intelligence. After witnessing the first Loebner Prize competition in 1991,
Stuart Shieber [20] concluded, “What is needed is not more work on
solving the Turing Test, as promoted by Loebner, but more work on
the basic issues involved in understanding intelligent behavior. The
parlor games can be saved for later” (p. 77). This conclusion seems
as valid today as it was two decades ago.

1.2 Communication, transparency, and the Turing
test
The question, then, is whether we might better capture the spirit of
Turing’s test through other, less literal-minded means. Our answer is
3
4

Of course, the year 2000 came and went without this prediction coming to
pass, but that is not at issue here.
See [5] for more discussion of the distinction between human-like intelligence versus other forms of intelligence in relation to the Turing test.

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not only that we can, but that we must. The alternative is to risk trivializing the test by equating “intelligence” with the ability to mimic
the sort of context-neutral conversation that has increasingly come to
pass for “communication.” Christian points out that “the Turing test
is, at bottom, about the act of communication” ([1], p. 13). Yet given
the two-way nature of communication, it can be hard to disentangle progress in one area (AI) from deterioration in others. As Jaron
Lanier recently put it,
You can’t tell if a machine has gotten smarter or if you’ve just
lowered your standards of intelligence to such a degree that the
machine seems smart. If you can have a conversation with a
simulated person presented by an AI program, can you tell how
far you’ve let your sense of personhood degrade in order to
make the illusion work for you? ([13], p. 32).
In short, the Turing test’s reliance on purely verbal behavior renders it susceptible to tricks and illusions that its creator could not
have reasonably anticipated. Methodologies such as statistical machine learning, while valuable as computational and engineering
tools, are nonetheless better suited to modeling human banality than
they are human intelligence. Additionally, the test, as currently instantiated, encourages an adversarial approach between contestant
and judge that does as much to obscure and inflate progress in AI as
it does to provide an accurate measuring stick. It is our contention
that a test that better meets Turing’s original intent should instead be
driven by the joint aims of collaboration and transparency.

2

INTELLIGENCE, TRICKERY, AND THE
LOEBNER PRIZE

Does deception presuppose intelligence on the part of the deceiver? In proposing his imitation game, Turing wagered—at least
implicitly—that the two were inseparable. Surely, a certain amount
of cunning and intelligence are required on the part of humans who
excel at deceiving others. The flip side of the coin is that a degree of
gullibility is required on the part of the person(s) being deceived.
Things get more complicated when the deception is “perpetrated”
by a technological artifact as opposed to a willfully deceptive human.
To quote Shieber once again, “[I]t has been known since Weizenbaum’s surprising experiences with ELIZA that a test based on fooling people is confoundingly simple to pass” (p. 72; cf. [24]). The gist
of Weizenbaum’s realization is that our interactions with computer
programs often tell us less about the inner workings of the programs
themselves than they do about our tendency to project meaning and
intention onto artifacts, even when we know we should know better.

2.1 The parallel case of art forgery
For another perspective on the distinction between genuine accomplishment and mere trickery, let us consider the parallel case of art
forgery. Is it possible to distinguish between a genuine artist and a
mere faker? It is tempting to reply that in order to be a good faker—
one good enough to fool the experts—one must necessarily be a good
artist to begin with. But this sort of argument is too simplistic, as it
equates artistry with technical skill and prowess, meanwhile ignoring originality, artistic vision, and other qualities that are essential to
genuine artistry (cf. [14], [2]). In particular, the ability of a skilled art
forger to create a series of works in the style of, say, Matisse does not
necessarily imply insight into the underlying artistic or expressive vision of Matisse—the vision responsible for giving rise to those works

in the first place. As philosopher Matthew Kieran succinctly puts it,
“There is all the difference in the world between a painting that genuinely reveals qualities of mind to us and one which blindly apes
their outward show” ([11], p. 21).
Russell’s famous quote about postulation equating to theft helps
us relate an AI methodology to the artistry–forgery distinction.
Russell’s statement can be paraphrased as follows: merely saying
that there exists a function (e.g., sqrt()) with some property
(e.g., sqrt(x)*sqrt(x)=x for all x >= 0) does not tell
us very much about how to generate the actual sqrt() function.
Similarly, the ability to reproduce a small number of values of x
that meet this specification does not imply insight into the underlying mechanisms involved, relative to which the existence of these
specific values is essentially a side effect. A key issue here is the
small number of values: Since contemporary versions of the Turing
test are generally highly time-constrained, it is even more imperative
that the test involve a deep probe into the possible behaviors of the
respondent.

2.2 Thematic variability in art and in computation
Many of the Loebner Prize entrants (e.g., [23]) have adopted the
methodologies of corpus linguistics and machine learning, so let us
reframe the issue of thematic variability in these terms. We might
abstractly consider the statistical machine-learning approach to the
Turing test as being concerned with the induction of a generative
grammar. In short, the ability to induce an algorithm that reproduces
some themed collection of original works does not in itself imply
that any underlying sensibilities that motivated those works can be
effectively approximated by that algorithm.
One way of measuring the “work capacity” of an algorithm is to
employ the Kolmogorov complexity measure [21], which is essentially the size of the shortest possible functionally identical algorithm. In the induction case, algorithms with the lowest Kolmogorov
complexity will tend to be those that exhibit very little variability—in
the limiting case, generating only instances from the original collection. This would be analogous to a forger who could only produce
exact copies of another artist’s works, rather than works “in the style
of” said artist—the latter being the stock-in-trade of infamous art
forgers Han van Meegeren [25] and Elmyr de Hory [10].
In contrast, programs from the family of Fluid Concepts architectures (see 4.1 below) possess relational and generative models that
are domain-specific. For example, the Letter Spirit architecture [19]
is specifically concerned with exploring the thematic variability of a
given font style. Given Letter Spirit’s (relatively) sophisticated representation of the “basis elements” and “recombination mechanisms”
of form, it might reasonably be expected to have a high Kolmogorov
complexity. The thematic variations generated by Letter Spirit are
therefore not easily approximated by domain-agnostic data-mining
approaches.

2.3 Depth, shallowness, and the Turing test
The artistry–forgery distinction is useful insofar as it offers another
perspective on the issue of depth versus shallowness—an issue that is
crucial in any analysis of the Turing test. Just as the skilled art forger
is adept at using trickery to simulate “authenticity”—for example, by
artificially aging a painting through various techniques such as baking or varnishing ([10], [25])—analogous forms of trickery tend to
find their way into the Loebner Prize competition: timely pop-culture
references, intentional typos and misspellings, strategic changes of

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subject, and so on (cf. [20], [1]). Yet these surface-level tricks have
as much to do with the genuine modeling of intelligence as coating
the surface of a painting with antique varnish has to do with bona fide
artistry. Much like the art forger’s relationship with the art world, the
relationship between contestant programs and judges in the Loebner
Prize is essentially adversarial, not collaborative. The adversarial nature of these contestant–judge interactions, we feel, is a driving force
in the divergence of the Turing test, in its current instantiations, from
the spirit in which it was originally conceived.

3

SOME VARIATIONS ON THE TURING TEST

The idea of proposing modifications to the Turing test is not a new
one. In this section, we look at such proposals—Stevan Harnad’s
“Total Turing Test” (and the accompanying hierarchy of Turing tests
he outlines) and Edward Feigenbaum’s eponymous variation on the
Turing test—before discussing how they relate to our own, described
below as a “modified Feigenbaum test.”

3.1 The Total Turing Test
Harnad ([6], [7]) has outlined a detailed hierarchy of possible Turing
tests, with Turing’s own version occupying the second of five rungs
on this hypothetical ladder. Harnad refers to this as the T2, or “penpal,” level, given the strict focus on verbal (i.e., written or typed)
output. Directly below this level is the t1 test (where “t” stands for
“toy,” not “Turing”). Harnad observed, a decade ago, that “all of the
actual mind-modelling research efforts to date are still only at the t1
level, and will continue to be so for the foreseeable future: Cognitive
Science has not even entered the TT hierarchy yet” ([7], §9). This is
still the case today.
Just as the t1 test draws on “subtotal fragments” of T2, T2 stands
in a similar relation to T3, the Total Turing Test. This test requires not
just pen-pal behavior, but robotic (i.e., embodied) behavior as well.
A machine that passed the Total Turing Test would be functionally
(though not microscopically) indistinguishable from a human being.5
Clearly, there are fewer degrees of freedom—and hence less room
for deception—as we climb the rungs on Harnad’s ladder, particularly from T2 to T3. However, given the current state of the art, the T3
can only be considered an extremely distant goal at this point. It may
be that the T2, or pen-pal, test could only be convincingly “passed”—
over an arbitrarily long period of time, as Harnad stipulates, and not
just the five-minute period suggested by Turing and adhered to in
the Loebner Prize competition—by a system that could move around
and interact with other people and things in the real world as we do.
It may even be that certain phenomena that are still being modeled
and tested at the t1 level—even seemingly abstract and purely “cognitive” ones such as analogy-making and categorization—are ultimately grounded in embodiment and sensorimotor capacities as well
(cf. [12]), which would imply fundamental limitations for much current research. Unfortunately, such questions must be set aside for the
time being, as they are beyond the scope of this paper.

3.2 The Feigenbaum test
The Feigenbaum test [3] was proposed in order test the quality
of reasoning in specialized domains—primarily scientific or otherwise technical domains such as astrophysics, computer science, and
medicine. The confederate in the Feigenbaum test is not merely an
5

The T4 and T5 levels, which make even greater demands, are not relevant
for our purposes.

ordinary human being, but an “elite scientist” and member of the U.S.
National Academy of Sciences. The judge, who is also an Academy
member and an expert in the domain in question, interacts with the
confederate and the contestant (i.e., the program). Feigenbaum elaborates, “The judge poses problems, asks questions, asks for explanations, theories, and so on—as one might do with a colleague” ([3],
p. 36). No time period is stipulated, but as with the Turing test, “the
challenge will be considered met if the computational intelligence
’wins’ one out of three disciplinary judging contests, that is, one of
the three judges is not able to choose reliably between human and
computer performer” (ibid.).

3.3 A modified Feigenbaum test
Feigenbaum’s emphasis on knowledge-intensive technical domains
is in keeping with his longtime work in the area of expert systems.
This aspect of his test is incidental, even irrelevant, to our purposes.
In fact, we go one step further with our “modified Feigenbaum test”
and remove the need for an additional contestant beyond the program. Rather, the judge “interacts” directly with the program for an
arbitrarily long period of time and evaluates the program’s behavior
directly—and qualitatively—on the basis of this interaction. (No illusion is made about the program passing for human, which would
be premature and naive in any case.)
What is relevant about the Feigenbaum test for our purposes is its
emphasis on focused, sustained interaction between judge and program within a suitably subtle domain. Our modified Feigenbaum test
stresses a similar type of interaction, though the domain—while still
constrained—is far less specialized or knowledge-intensive than, say,
astrophysics or medicine. In fact, the domain we discuss below—
letter-string analogies—was originally chosen as an arena for modeling cognition because of its balance of generality and tractability
[9]. In other words, the cognitive processes involved in thinking and
otherwise “operating” within the domain are intended to be more or
less general and domain-independent. At the same time, the restriction of the domain, in terms of the entities and relationships that make
it up, is meant to ensure tractability and plausibility—in contrast to
dealing (or pretending to deal) with complex real-world knowledge
of a sort that can scarcely be attributed to a computer program (e.g.,
knowledge of medicine, the solar system, etc.).
In the following section, we argue on behalf of this approach and
show how research carried out under this ongoing program represents
an example of how one can take the idea of Turing’s test seriously
without taking its specifications literally.

4

TAKING TURING SERIOUSLY: AN
ALTERNATIVE APPROACH

In an essay entitled “On the Seeming Paradox of Mechanizing Creativity,” Hofstadter [8] relates Myhill’s [17] three classes of mathematical logic to categories of behavior. The most inclusive category,
the productive, is the one that is of central interest to us here. While
no finite collection of rules suffices to generate the members of a productive set P (and no x ∈
/ P ), a more expansive and/or sophisticated
set of generative rules (i.e., creative processes) can approximate P
with unbounded accuracy.
In order to emphasize the role of such “unbounded creativity” in
the evaluation of intelligence, we describe a modified Feigenbaum
test restricted to the microdomain of letter-string analogies. An example of such a problem is, “If abc changes to abd, how would you
change pxqxrx in ’the same way’?” (or simply abc → abd; pxqxrx

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→ ???). Problems in this domain have been the subject of extensive study [9], resulting in the creation of the well-known Copycat
model [16] and its successor, Metacat [15]. Before describing this
test, however, we briefly discuss these programs’ architectures in
general terms.

4.1 Copycat, Metacat, and Fluid Concepts
architectures
Copycat’s architecture consists of three main components, all of
which are common to the more general Fluid Concepts architectural
scheme. These components are the Workspace, which is essentially
roughly the program’s working memory; the Slipnet, a conceptual
network with variably weighted links between concepts (essentially
a long-term memory); and the Coderack, home to a variety of agentlike codelets, which perform specific tasks in (simulated) parallel,
without the guidance of an executive controller. For example, given
the problem abc → abd; iijjkk → ???, these tasks would range from
identifying groups (e.g., the jj in iijjkk) to proposing bridges between items in different letter-strings (e.g., the b in abc and the jj in
iijjkk) to proposing rules to describe the change in the initial pair of
strings (i.e., the change from abc to abd).6
Building on Copycat, Metacat incorporates some additional components that are not present in its predecessor’s architecture, most
notably the Episodic Memory and the Temporal Trace. As the program’s name suggests, the emphasis in Metacat is on metacognition,
which can broadly be defined as the process of monitoring, or thinking about, one’s own thought processes. What this means for Metacat
is an ability to monitor, via the Temporal Trace, events that take place
en route to answering a given letter-string problem, such as detecting a “snag” (e.g., trying to find the successor to z, which leads to a
snag because the alphabet does not “circle around” in this domain)
or noticing a key idea. Metacat also keeps track of its answers to previous problems, as well as its responses on previous runs of the same
problem, both via the Episodic Memory. As a result, it is able to be
“reminded” of previous problems (and answers) based on the problem at hand. Finally, it is able to compare and contrast two answers
at the user’s prompting (see Section 4.3 below).
Philosophically speaking, Fluid Concepts architectures are predicated upon the conviction that it is possible to “know everything
about” the entities and relationships in a given microdomain. In other
words, there is no propositional fact about domain entities and processes (or the effect of the latter on the on the former) that is not in
principle accessible to inspection or introspection. In Copycat, the
domain entities range from permanent “atomic” elements (primarily,
the 26 letters of the alphabet) to temporary, composite ones, such as
the letter strings that make up a given problem (abc, iijjkk, pxqxrx,
etc.); the groups within letter strings that are perceived during the
course of a run (e.g., the ii, jj, and kk in iijjkk); and the bonds that
are formed between such groups. The relationships include concepts
such as same, opposite, successor, predecessor, and so on.
A key aspect of the Fluid Concepts architecture is that it affords an
exploration the space of instantiations of those entities and relationships in a (largely) non-stochastic fashion—that is, in a manner that is
predominately directed by the nature of the relationships themselves.
In contrast, the contextual pressures that give rise to some subtle yet
low frequency solutions are unlikely to have a referent within a statistical machine-learning model built from a corpus of Copycat an6

See [16] for an in-depth discussion of codelet types and functions in Copycat.

swers, since outliers are not readily captured by gross mechanisms
such as sequences of transition probabilities.

4.2 An example from the Copycat microdomain
To many observers, a letter-string analogy problem such as the aforementioned abc → abd; iijjkk → ??? might appear trivial on first
glance.7 Yet upon closer inspection, one can come to appreciate the
surprising subtleties involved in making sense of even a relatively
basic problem like this one. Consider the following (non-exhaustive)
list of potential answers to the above problem:
• iijjll – To arrive at this seemingly basic answer requires at least
three non-trivial insights: (1) seeing iijjkk as a sequence of three
sameness groups—ii, jj, and kk—not as a sequence of individual
letters; (2) seeing the group kk as playing the same role in iijjkk
that the letter c does in abc; and (3) seeing the change from c to
d in terms of successorship and not merely as a change from the
letter c to the letter d. The latter point may seem trivial, but it is not
a given, and as we will see, there are other possible interpretations.
• iijjkl – This uninspiring answer results from simply changing the
letter category of the rightmost letter in iijjkk to its successor, as
opposed to the letter category of the rightmost group.
• iijjkd – This answer results from the literal-minded strategy of
simply changing the last letter in the string to d, all the while ignoring the other relationships among the various groups and letter
categories.
• iijjdd – This semi-literal, semi-abstract answer falls somewhere
in between iijjll and iijjkl. On the one hand, it reflects a failure to
perceive the change from c to d in the initial string in terms of successorship, instead treating it as a mere replacement of the letter
c with the letter d. On the other hand, it does signal a recognition
that the concept group is important, as it at least involves carrying
out the change from k to d in the target string over to both ks and
not just the rightmost one. This answer has a “humorous” quality to it, unlike iijjkl or iijjkd, due to its mixture of insight and
confusion.
This incomplete catalog of answers hints at the range of issues that
can arise in examining a single problem in the letter-string analogy
domain. Copycat itself is able to come up with all of the aforementioned answers (along with a few others), as illustrated in Table 1,
which reveals iijjll to be the program’s “preferred choice” according to the two available measures. These measures are (1) the relative frequency with which each answer is given and (2) the average
“final temperature” associated with each answer. Roughly speaking,
the temperature—which can range from 0 to 100—indicates the program’s moment-to-moment “happiness” with its perception of the
problem during a run, with a lower temperature corresponding to a
more positive evaluation

4.3 The modified Feigenbaum test: from Copycat
to Metacat
One limitation of Copycat is its inability to “say” anything about the
answers it gives beyond what appears in its Workspace during the
7

Such problems may seem to bear a strong resemblance to the kinds of problems one might find on an IQ test. However, an important difference worth
noting is that the problems in the Copycat domain are not conceived of as
having “correct” or “incorrect” answers (though in many cases there are
clearly “better” and “worse” ones). Rather, the answers are open to discussion, and the existence of subtle differences between the various answers to
a given problem is an important aspect of the microdomain.

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Table 1. Copycat’s performance over 1000 runs on the problem abc →
abd; iijjkk → ???. Adapted from [16].
Answer
iijjll
iijjkl
iijjdd
iikkll
iijkll
iijjkd
ijkkll

Frequency

Average Final Temperature

810
165
9
9
3
3
1

27
47
32
46
43
65
43

course of a run. While aggregate statistics such as those illustrated
in Table 1 can offer some insight into its performance, the program
is not amenable to genuine Feigenbaum-testing, primarily because it
doesn’t have the capacity to summarize its viewpoint. To the extent
that it can be Feigenbaum-tested, it can only do so in response to
what might termed first-order questions (e.g., abc → abd; iijjkk →
???). It cannot answer second-order questions (i.e., questions about
questions), let alone questions about its answers to questions about
questions.
In contrast, Metacat allows us to ask increasingly sophisticated
questions of it, and thus can be said to allow for the sort of modified Feigenbaum-testing described in Section 3.3. One can “interact”
with the program in a variety of ways: by posing new problems; by
inputting an answer to a problem and running the program in “justify
mode,” asking it to evaluate and make sense of the answer; and by
having it compare two answers to one another (as in the above examples). In doing the latter, the program summarizes its “viewpoint”
with one of a set of canned (but non-arbitrary) English descriptions.
For example, the preferred answer might be “based on a richer set of
ideas,” “more abstract,” or “more coherent.”
The program also attempts to “explain” how the two answers are
similar to each other and how they differ. For example, consider the
program’s summary of the comparison between iijjll and iijjdd in
response to the aforementioned problem:
The only essential difference between the answer iijjdd and
the answer iijjll to the problem abc → abd; iijjkk → ??? is that
the change from abc to abd is viewed in a more literal way for
the answer iijjdd than it is in the case of iijjll. Both answers rely
on seeing two strings (abc and iijjkk in both cases) as groups
of the same type going in the same direction. All in all, I’d say
iijjll is the better answer, since it involves seeing the change
from abc to abd in a more abstract way.
It should be emphasized that the specific form of the verbal output
is extremely unsophisticated relative to the capabilities of the underlying architecture, indicating that it is possible to exhibit depth of
insight while treating text generation as essentially a side-effect. This
contrasts sharply with contemporary approaches to the Turing test.
For the sake of contrast, here is the program’s comparison between
the answers iijjll and abd, which illustrates some of the program’s
limitations in clumsily (and, of course, unintentionally) humorous
fashion:
The only essential difference between the answer abd and
the answer iijjll to the problem abc → abd; iijjkk → ??? is
that the change from abc to abd is viewed in a completely different way for the answer abd than it is in the case of iijjll.
Both answers rely on seeing two strings (abc and iijjkk in both

cases) as groups of the same type going in the same direction.
All in all, I’d say abd is really terrible and iijjll is very good.
Apart from the thin veneer of human agency that results from
Metacat’s text generation, the program’s accomplishments—and just
as importantly, its failures—become transparent through interaction.

4.4 Looking ahead
In order for it to actually pass an “unrestricted modified Feigenbaum
test” in the letter-string analogy domain, what other questions might
we conceivably require Metacat to answer? Here are some suggestions:
1. Problems that involve more holistic processing of letter strings.
There are certain letter strings that humans seem to have little
trouble processing, but that are beyond Metacat’s grasp—for example, the string oooaaoobboooccoo in the problem abc → abd;
oooaaoobboooccoo → ???. How are we so effortlessly able to
“tune out” the o’s in oooaaoobboooccoo? What would it take for
a Metacat-style program to be able to do likewise?
2. Meta-level questions about sequences of answers. For example,
“How is the relationship between answer A and answer B different
from that between C and D?” Such questions could be answered
using the declarative information that Metacat already has; all that
would seem to be required is the ability to pose the question.
3. Questions pertaining to concepts about analogy-making in general, such as mapping, role, theme, slippage, pressure, pattern,
and concept. Metacat deals implicitly with all of these ideas, but
it doesn’t have explicit knowledge or understanding of them.
4. An ability to characterize problems in terms of “the issues they
are about,” with the ultimate goal of having a program that is able
to create new problems of its own—which would certainly lead
to a richer, more interesting exchange between the program and
the human interacting with it. Some work in this area was done in
the Phaeaco Fluid Concepts architecture [4], but the issue requires
further investigation.
5. Questions of the form, “Why is answer A more humorous (or
stranger, or more elegant, etc.) than answer B?” Metacat has implicit notions, however primitive, of concepts such as succinctness, coherence, and abstractness, which figure into its answer
comparisons. These notions pertain to aesthetic judgment insofar
as we tend to find things that are succinct, coherent, and reasonably abstract to be more pleasing than things that are prolix, incoherent, and either overly literal or overly abstract. Judgments
involving humor often take into account such factors, too, among
many others. Metacat’s ability—however rudimentary—to employ criteria such as abstractness and coherence in its answer evaluations could be seen as an early step toward understanding how
these kinds of qualitative judgments might emerge from simpler
processes. On the other hand, for adjectives such as “humorous,”
which presuppose the possession of emotional or affective states,
it is not at all clear what additional mechanisms might be required,
though some elementary possibilities are outlined in [18].
6. A rudimentary sense of the “personality traits” associated with
certain patterns of answers. In other words, just as Metacat is able
compare two answers with one another, a meta-Metacat might
be able to compare two sets of answers—and, correspondingly,
two answerers—with one another. For example, a series of literalminded or short-sighted answers might yield a perception of the
answerer as being dense, while a series of sharp, insightful an-

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58

swers punctuated by the occasional obvious clunker might yield a
picture of an eccentric smart-aleck.
Ultimately, however, the particulars of Copycat, Metacat, and the
letter-string analogy domain are not so important in and of themselves. The programs merely serve as an example of a kind of approach to modeling cognitive phenomena, just as the domain itself
serves as a controlled arena for carrying out such modeling.
To meet the genuine intent of the Turing test, we must be able
to partake in the sort of arbitrarily detailed and subtle discourse
described above in any domain. As the forgoing list shows, however, there is much that remains to be done, even—to stick with our
example—within the tiny domain in which Copycat and Metacat operate. It is unclear how far a disembodied computer program, even
an advanced successor to these two models, can go toward modeling
socially and/or culturally grounded phenomena such as personality,
humor, and aesthetic judgment, to name a few of the more obvious
challenges involved in achieving the kind of discourse that our “test”
ultimately calls for. At the same time, it is unlikely that such discourse lies remotely within the capabilities of any of the current generation of Loebner Prize contenders, nor does it even seem to be a
goal of such contenders.

5

CONCLUSION

We have argued that the Turing test would more profitably be considered as a sequence of modified Feigenbaum tests, in which the questioner and respondent are to collaborate in an attempt to extract maximum subtlety from a succession of arbitrarily detailed domains. In
addition, we have explored a parallel between the “domain-agnostic”
approach of statistical machine learning and that of artistic forgery,
in turn arguing that by requesting successive variations on an original theme, a critic may successfully distinguish mere surface-level
imitations from those that arise via the meta-mechanisms constitutive of genuine creativity and intelligence. From the perspective we
have argued for, Metacat and the letter-string-analogy domain can be
viewed as a kind of Drosophila for the Turing test, with the search
for missing mechanisms directly motivated by the specific types of
questions we might conceivably ask of the program.

[8] D. R. Hofstadter, Metamagical Themas: Questing for the Essence of
Mind and Pattern, Basic Books, New York, 1986.
[9] D. R. Hofstadter, Fluid Concepts and Creative Analogies, Basic Books,
New York, 1995.
[10] C. Irving, Fake! The story of Elmyr de Hory, the greatest art forger of
our time, McGraw-Hill, New York, 1969.
[11] M. Kieran, Revealing Art, Routledge, London, 2005.
[12] B. Kokinov, V. Feldman, and I. Vankov, ‘Is analogical mapping embodied?’, in New Frontiers in Analogy Research, eds., B. Kokinov,
K. Holyoak, and D. Gentner, New Bulgarian Univ. Press, Sofia, Bulgaria, (2009).
[13] J. Lanier, You Are Not a Gadget, Alfred A. Knopf, New York, 2010.
[14] A. Lessing, ‘What is wrong with a forgery?’, Journal of Aesthetics and
Art Criticism, 23(4), 461–471, (1979).
[15] J. Marshall. Metacat: A self-watching cognitive architecture for
analogy-making and high-level perception. Doctoral dissertation, Indiana Univ., Bloomington, 1999.
[16] M. Mitchell, Analogy-Making as Perception: A Computer Model, MIT
Press, Cambridge, Mass., 1993.
[17] J. Myhill, ‘Some philosophical implications of mathematical logic’, Review of Metaphysics, 6, 165–198, (1952).
[18] R. Picard, Affective Computing, MIT Press, Cambridge, Mass., 1997.
[19] J. Rehling. Letter spirit (part two): Modeling creativity in a visual domain. Doctoral dissertation, Indiana Univ., Bloomington, 2001.
[20] S. Shieber, ‘Lessons from a restricted Turing test’, Communications of
the ACM, 37(6), 70–78, (1994).
[21] R.J. Solomonoff, ‘A formal theory of inductive inference, pt. 1’, Information and Control, 7(1), 1–22, (1964).
[22] A. Turing, ‘Computing machinery and intelligence’, Mind, 59, 433–
460, (1950).
[23] R. Wallace, ‘The anatomy of A.L.I.C.E.’, in Parsing the Turing Test,
eds., R. Epstein, G. Roberts, and G. Beber, 1–57, Spring, Heidelberg,
(2009).
[24] J. Weizenbaum, Computer Power and Human Reason, Freeman, San
Francisco, 1976.
[25] H. Werness, ‘Han van Meegeren fecit’, in The Forger’s Art, ed., D. Dutton, 1–57, Univ. of California Press, Berkeley, (1983).

ACKNOWLEDGEMENTS
We would like to thank Vincent Müller and Aladdin Ayesh for their
hard work in organizing this symposium, along with the anonymous
referees who reviewed and commented on the paper. We would also
like to acknowledge the generous support of Indiana University’s
Center for Research on Concepts and Cognition.

REFERENCES
[1] B. Christian, The Most Human Human, Doubleday, New York, 2011.
[2] D. Dutton, ‘Artistic crimes’, British Journal of Aesthetics, 19, 302–314,
(1979).
[3] E. A. Feigenbaum, ‘Some challenges and grand challenges for computational intelligence’, Journal of the ACM, 50(1), 32–40, (2003).
[4] H. Foundalis. Phaeaco: A cognitive architecture inspired by bongard’s
problems. Doctoral dissertation, Indiana Univ., Bloomington, 2006.
[5] R. French, ‘Subcognition and the limits of the Turing test’, Mind, 99,
53–65, (1990).
[6] S. Harnad, ‘The Turing test is not a trick: Turing indistinguishability is
a scientific criterion’, SIGART Bulletin, 3(4), 9–10, (1992).
[7] S. Harnad, ‘Minds, machines and Turing: the indistinguishability of indistinguishables’, Journal of Logic, Language, and Information, 9(4),
425–445, (2000).

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Laws of Form and the Force of Function.
Variations on the Turing Test
Hajo Greif1
Abstract. This paper commences from the critical observation that
the Turing Test (TT) might not be best read as providing a definition or a genuine test of intelligence by proxy of a simulation of
conversational behaviour. Firstly, the idea of a machine producing
likenesses of this kind served a different purpose in Turing, namely
providing a demonstrative simulation to elucidate the force and scope
of his computational method, whose primary theoretical import lies
within the realm of mathematics rather than cognitive modelling.
Secondly, it is argued that a certain bias in Turing’s computational
reasoning towards formalism and methodological individualism contributed to systematically unwarranted interpretations of the role of
the TT as a simulation of cognitive processes. On the basis of the
conceptual distinction in biology between structural homology vs.
functional analogy, a view towards alternate versions of the TT is
presented that could function as investigative simulations into the
emergence of communicative patterns oriented towards shared goals.
Unlike the original TT, the purpose of these alternate versions would
be co-ordinative rather than deceptive. On this level, genuine functional analogies between human and machine behaviour could arise
in quasi-evolutionary fashion.

1

A Turing Test of What?

While the basic character of the Turing Test (henceforth TT) as a simulation of human conversational behaviour remains largely unquestioned in the sprawling debates it has triggered, there are a number
of diverging interpretations as to whether and to what extent it provides a definition, or part of a definition, of intelligence in general,
or whether it amounts to the design of an experimental arrangement
for assessing the possibility of machine intelligence in particular. It
thus remains undecided what role, if any, there is for the TT to play
in cognitive inquiries.
I will follow James H. Moor [13] and other authors [21, 2] in their
analysis that, contrary to seemingly popular perception, the TT does
neither provide a definition nor an empirical criterion of the named
kind. Nor was it intended to do so. At least at one point in Alan M.
Turing’s, mostly rather informal, musings on machine intelligence,
he explicitly dismisses the idea of a definition, and he attenuates the
idea of an empirical criterion of machine intelligence:
I don’t really see that we need to agree on a definition [of thinking] at all. The important thing is to try to draw a line between
the properties of a brain, or of a man, that we want to discuss,
and those that we don’t. To take an extreme case, we are not
interested in the fact that the brain has the consistency of cold
porridge. We don’t want to say ‘This machine’s quite hard, so
1

University of Klagenfurt, Austria, email: hajo.greif@aau.at

it isn’t a brain, and so it can’t think.’ I would like to suggest
a particular kind of test that one might apply to a machine.
You might call it a test to see whether the machine thinks, but
it would be better to avoid begging the question, and say that
the machines that pass are (let’s say) ‘Grade A’ machines. [. . . ]
(Turing in a BBC radio broadcast of January 10th, 1952, quoted
after [3, p. 494 f])
Turing then goes on to introducing a version of what has come to
be known, perhaps a bit unfortunately, as the Turing Test, but was
originally introduced as the “imitation game”. In place of the articulation of definitions of intelligence or the establishment of robust empirical criteria for intelligence, we find much less ambitious,
and arguably more playful, claims. One purpose of the test was to
develop a thought-experimental, inductive approach to identifying
those properties shared between the human brain and a machine
which would actually matter to asking the question of whether men
or machines alike can think: What is the common ground human
beings and machines would have to share in order to also share a
set of cognitive traits? It was not a matter of course in Turing’s day
that there could possibly be any such common ground, as cognition
was mostly considered essentially tied to (biological or other) human nature.2 In many respects, the TT was one very instructive and
imaginative means of raising the question whether the physical constitution of different systems, whether cold-porrige-like or electriccircuitry-like, makes a principled difference between a system with
and a system without cognitive abilities. Turing resorted to machine
simulations of behaviours that would normally be considered expressions of human intelligence in order to demonstrate that the lines of
demarcation between the human and the mechanical realm are less
than stable.
The TT is however not sufficient as a means for answering the
questions it first helped to raise, nor was it so intended. Turing’s
primary aim for the TT was one demonstration, among others, of
the force and scope of what he introduced as the “computational
method” (which will be briefly explained in section 2). Notably,
the computational method has a systematically rooted bias towards,
firstly, considering a system’s logical form over its possible functions
and towards, secondly, methodological individualism. I will use Turing’s mathematical theory of morphogenesis and, respectively, the
distinction between the concepts of structural homology and functional analogy in biology as the background for discussing the implications of this twofold bias (in section 3). On the basis of this discussion, a tentative reassessment of the potentials and limits of the
2

In [1, p. 168 f], Margaret Boden notices that the thought that machines could
possibly think was not even a “heresy” up to the early 20th century, as that
claim would have been all but incomprehensible.

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TT as a simulation will be undertaken (in section 4): If there is a systematic investigative role to play in cognitive inquiries for modified
variants of the TT, these would have to focus on possible functions
to be shared between humans and machines, and they would have
to focus on shared environments of interaction rather than individual
behaviours.

2

The Paradigm of Computation

Whether intentionally or not, Turing’s reasoning contributed to
breaking the ground for the functionalist arguments that prevail in
much of the contemporary philosophies of biology and mind: An
analysis is possible of the operations present within a machine or an
organism that systematically abstracts from their respective physical
nature. An set of operations identical on a specified level of description can be accomplished in a variety of physical arrangements. Any
inference from the observable behavioural traits of a machine simulating human communicative behaviour, as in the TT, to an identity
of underlying structural features would appear unwarranted.
Turing’s work was concerned with the possibilities of devising a
common logical form of abstractly describing the operations in question. His various endeavours, from morphogenesis via (proto-) neuronal networks to the simulation of human conversational behaviour,
can be subsumed under the objective of exploring what his “computational method” could achieve across a variety of empirical fields
and under a variety of modalities. Simulations of conversational behaviours that had hitherto been considered an exclusively human
domain constituted but one of these fields, investigated under one
modality.
Turing’s computational method is derived from his answer to a
logico-mathematical problem, David Hilbert’s “Entscheidungsproblem” (the decision problem) in predicate logic, as presented in [8].
This problem amounts to the question whether, within the confines
of a logical calculus, there is an unequivocal, well-defined and finite, hence at least in principle executable, procedure for deciding on
the truth of a proposition stated in that calculus. After Kurt Gödel’s
demonstration that neither the completeness nor the consistency of
arithmetic could be proven or disproven within the confines of arithmetic proper [7], the question of deciding on the truth of arithmetical propositions from within that same axiomatic system had to be
recast as a question of deciding on the internal provability of such
propositions. The – negative – answer to this reformulated problem was given by Turing [18] (and, a little earlier, by a slightly different method, Alonzo Church). Turing’s path towards that answer
was based on Gödel’s elegant solution to the former two problems,
namely a translation into arithmetical forms of the logical operations
required for deciding on the provability of that proposition within
the system of arithmetical axioms. Accordingly, the method of further investigation was to examine the calculability of the arithmetical
forms so generated.
To decide on the calculability of the problem in turn, Turing introduced the notion of computability. A mathematical problem is considered computable if the process of its solution can be broken down
into a set of exact elementary instructions by which one will arrive at
a determinate solution in a finite number of steps, and which could
be accomplished, at least in principle, by human “computers”.3 Even
complex problems should thus become reducible to a set of basic
3

I am following B. Jack Copeland [4] here on his definition of computability,
as he makes a considerable effort at spelling out what notion of computability Turing was using in [18]. He thus hopes to stem the often-lamented flood
of loose and misguiding uses of that term in many areas of science.

operations. The fulfilment of the required routines demands an ability to apply a set of rules and, arguably, some mental discipline, but
these routines are not normally considered part of the most typical
or complex properties of human thought – and can be mechanised,
in a more direct, material sense, by an appropriately constructed and
programmed machine. Hence, Turing’s notion of “mechanical” was
of a fairly abstract kind. It referred to a highly standardised and routinised method of solving mathematical problems, namely the computational method proper. This method could be equally applied by
human, mechanical or digital “computers”, or by any other system
capable of following the required routines.
Given this description of computability, the primary aim of Turing’s models of phenomena such as morphogenesis, the organisation
of the nervous system or the simulation of human conversation lies in
finding out whether, how and to what extent their specific structural
or behavioural patterns can be formally described in computational
terms – and thus within the realm of mathematics. A successful application of the computational method to the widest variety of phenomena would have implications on higher epistemological or arguably
even metaphysical levels, but, being possible implications, these are
not contained within the mathematical theory.

3

The Relevance of Form and Function

The design of Turing’s computational method intuitively suggests,
but does not entail, that the phenomena in question are chiefly considered in their, computationally modellable, form. Turing focuses
on the formal patterns of organic growth, on the formal patterns of
neuronal organisation and re-organisation in learning, and on the logical forms of human conversation. The possible or actual functions
of these formally described patterns, in terms of the purposes they do
or may serve, are not systematically considered. A second informal
implication of Turing’s computational approach lies in his focus on
the behaviour of isolated, individual systems – hence not on the organism in its environment, but on the human brain as a device with
input and output functions.4 Such focus on self-contained, individual entities was arguably guided by a methodological presupposition
informed by the systematic goals of Turing’s research: The original
topics of his inquiry were the properties of elementary recursive operations within a calculus. Hence, any empirical test for the force
and scope of the computational method, that is, any test for what can
be accomplished by means of such elementary recursive operations,
would naturally but not necessarily commence in the same fashion.
In order to get a clearer view of this twofold bias, it might be
worthwhile to take a closer look at the paradigm of Turing’s computational method. That paradigm, in terms of elaboration, rigour and
systematicity, is not to be found in his playful and informal imitation
game approach to computer simulations of conversational behaviour.
Instead, it is to be found in his mathematical theory of morphogenesis [20]. This inquiry was guided by Sir D’Arcy Thompson’s, at
its time, influential work On Growth and Form [17], and it was directed at identifying the basic chemical reactions involved in generating organic patterns, from an animal’s growth to the grown animal’s
anatomy, from the dappledness or stripedness of furs to the arrangement of a sunflower’s florets and the phyllotactic ordering of leaves
on a plant’s twigs. The generation of such patterns was modelled
in rigorously formal-mathematical fashion. The resulting model was
impartial to the actual biochemical realisation of pattern formation. It
would only provide some cues as to what concrete reactants, termed
“morphogens” by Turing, one should look out for.
4

For this observation, see, for example, [9, p. 85].

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Less obviously but similarly important, Turing chose not to inquire into any adaptive function, in Darwinian terms, of the patterns
so produced. These patterns may or may not serve an adaptive function, and what that function amounts to is of secondary concern at
best. Explaining the generation of their form does not contribute to
explaining that form’s function, nor does it depend on that function.
In this respect, too, Turing’s thoughts appear to be in line with, if
not explicitly endorsing, D’Arcy Thompson’s skeptical view of the
relevance of adaptation by natural selection in evolution. The formative processes in organisms are considered at least partly autonomous
from Darwinian mechanisms. Whether the florets of a sunflower are
patterned on a Fibonacci series, as they in fact are, or whether they
are laid out in grid-like fashion, as they possibly cannot be according
to the mathematical laws of form expounded by Turing, is unlikely
to make a difference in terms of selective advantage. In turn however, natural selection may not offer a path to a grid-like pattern in
the first place, while enabling, but arguably not determining, the Fibonacci pattern. In likewise fashion, the cognitive abilities of human
beings or other animals would not in the first place be considered
as adaptive abilities, defined in relation to challenges posed by their
environments, but in their, mathematically modellable, form.
Turing’s bias towards form over function, in conjunction with his
methodological individualism, created a difficulty in systematically
grasping a relation that might look straightforward or even obvious to
the contemporary reader, who is likely to be familiar with the role of
populations and environments in evolution, and who might also be familiar with philosophical concepts of functions: analogy of functions
across different, phylogenetically distant species. In Turing’s notion
of decoupling logical form from physical structure, the seeds of the
concept of functional analogy appear to be sown, however without
growing to a degree of maturity that would prevent the premature
conclusions often drawn from Turing’s presentation of the TT.
It is the condition of observable similarity in behaviour that has
been prone to misguide both proponents and critics of the TT. One
cannot straightforwardly deduce a similarity of kind – in this case,
being in command of a shared form of intelligence – from a similarity in appearance. A relation of proximity in kind could only be
firmly established on the grounds of a relation of common descent,
that is, from being part of the same biological population or from
being assembled according to a common design or Bauplan. This is
the ultimate skeptical resource for the AI critic who will never accept some computer’s or robot’s trait as the same or equivalent to
a human one. However convincing it may look to the unprejudiced
observer, any similarity will be dismissed as a feat of semi-scientific
gimmickry. Even a 1:1 replica of a human being, down to artificial
neurones and artificial muscles made of high-tech carbon-based fibres, is unlikely to convince him or her. What the skeptic is asking
for is a structural homology to lie at the foundation of observable
similarities.
In the biological discipline of morphology, the distinction between
analogies and homologies has first been systematically applied by
Richard Owen, who defined it as follows:
“A NALOGUE.” – A part or organ in one animal which has the
same function as another part or organ in a different animal.
“H OMOLOGUE.” – The same organ in different animals under
every variety of form and function. [15, p. 7, capitalisation in
original]
This distinction was put on an evolutionary footing by Charles Darwin, who gave a paradigmatic example of homology himself, when
he asked: “What can be more curious than that the hand of a man,

formed for grasping, that of a mole for digging, the leg of the horse,
the paddle of the porpoise, and the wing of the bat, should all be
constructed on the same pattern, and should include the same bones,
in the same relative positions?” [5, p. 434] – where the reference of
“the same” for patterns, bones and relative positions is fixed by their
common ancestral derivation rather than, for Owen and other Natural
Philosophers of his time, by abstract archetypes.
In contrast, an analogy of function of traits or behaviours amounts
to a similarity or sameness of purpose which a certain trait or behaviour serves, but which, firstly, may be realised in phenotypically
variant form and which, secondly, will not have to be derived from
a relation of common descent. For example, consider the function
of vision in different species, which is realised in a variety of eye
designs made from different tissues, and which is established along
a variety of lines of descent. The most basic common purpose of
vision for organisms is navigation within their respective environments. This purpose is shared by camera-based vision in robots, who
arguably have an aetiology very different from any natural organism.
Conversely, the same navigational purpose is served by echolocation
in bats, which functions in an entirely different physical medium and
under entirely different environmental circumstances, namely the absence of light.
There are no principled limitations as to how a kind of function is
realised and by what means it is transmitted. The way in which either
variable is fixed depends on the properties of the (biological or technological) population and of the environment in question. In terms
of determining its content, a function is fixed by the relation between
an organism’s constitution and the properties of the environment in
which it finds itself, and thus by what it has to accomplish in relation to organic and environmental variables in order to prevail. This
very relation may be identical despite the constitution of organisms
and the properties of the environment being at variance between different species. Perceiving spatial arrangements in order to locomote
under different lighting conditions would be a case in point. In terms
of the method by which a function is fixed, a history of differential
reproduction of variant traits that are exposed to the variables of the
environment in which some population finds itself will determine the
functional structure of those traits. If an organism is endowed with a
reproducible trait whose effects keep in balance those environmental
variables which are essential to the organism’s further existence and
reproduction, and if this happens in a population of reproducing organisms with sufficient frequency (which does not even have to be
extremely high), the effects of that trait will be their functions.5
Along the lines of this argument, an analogy of function is possible
between different lines of descent, provided that the environmental
challenges for various phylogenetically remote populations are similar. There are no a-priori criteria by which to rule out the possibility
that properties of systems with a common descent from engineering
processes may be functionally analogous to the traits and behaviours
of organisms. In turn, similarity in appearance is at most a secondary
consequence of functional analogy. Although such similarity is fairly
probable to occur, as in the phenomenon of convergent evolution, it is
never a necessary consequence of functional analogy. The similarity
that is required to hold between different kinds of systems lies in the
tasks for whose fulfilment their respective traits are selected. Structural homology on the other hand does neither require a similarity of
tasks nor a similarity of appearance, but a common line of descent
from which some trait hails, whatever function it may have acquired
later along that line, and whatever observable similarity it may bear
5

This is the case for aetiological theories of function, as pioneered by [23]
and elaborated by [11].

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to its predecessor. In terms of providing criteria of similarity that go
beyond what can be observed on the phenotypical level, functional
analogy trumps structural homology.

4

The Turing Test as Demonstrative vs.
Investigative Simulation

On the grounds of the above argument, the apparent under-definition
of the epistemical role of the TT owes to an insufficient understanding of the possibilities and limitations of functional analogy in the AI
debates: It is either confounded with homological relations, which,
as there are no common lines of descent between human beings and
computers, results in the TT being rejected out of hand as a test for
any possible cognitive ability of the latter. Or analogous functions are
considered coextensive with a set of phenotypical traits similar, qua
simulation, to those of human beings. Either way, it shows that inferences to possible cognitive functions of the traits in question are not
warranted by phenotypical similarity. Unless an analogy of function
can be achieved, the charge of gimmickry against the TT cannot be
safely defused. If however such an analogy can be achieved, the test
itself would not deliver the evidence necessary for properly assessing
that analogy, nor would it provide much in the way of a suggestion
how that analogy could be traced.
One might be tempted to put the blame for this insufficient understanding of functional analogy on Turing himself – but that might
be an act of historical injustice. Firstly, he did not claim functional
analogies to be achieved by his simulations. Secondly, some of the
linkages between the formal-mathematical models which he developed and more recent concepts of evolution that comprise the role
of populations and environments in shaping organic functions were
not in reach of his well-circumscribed theory of computation. They
were not even firmly in place at the time of his writing. Much of contemporary evolutionary reasoning owes to the Modern Synthesis in
evolutionary biology, which was only in the process of becoming the
majority view among biologists towards the end of Turing’s life.6
With the benefit of hindsight however, and with the clarification of
matters that it allows, is there any role left for the TT to be played
in inquiries into human cognition – which have to concern, first and
foremost, the functions of human cognition? Could it still function as
a simulation of serious scientific value? Or, trying to capture Turing’s
ultimate, trans-mathematical objective more precisely and restating
the opening question of this paper: Could the TT still help to identify
the common ground human beings and machines would have to share
in order to also share a set of cognitive traits? For modified forms of
that test at least, the answer might be positive.
First of all, one should be clear about what kind of simulation the
TT is supposed to be. If my reconstruction of Turing’s proximate
aims is valid, the imitation game was intended as a demonstrative
simulation of the force and scope of the computational method, with
no systematic cognitive intent. By many of its interpreters and critics
however, it was repurposed as an investigative simulation that, at a
minimum, tests for some of the behavioural cues by which people
normally discern signals of human intelligence in communication,
or that, on a maximal account, test for the cognitive capacities of
machines proper.
The notions of demonstrative and investigative simulations are distinguished in an intuitive, prima facie fashion in [16, p. 7 f], but
may not always be as clearly discernible as one might hope. Demonstrative simulations mostly serve a didactic purpose, in reproducing

some well-known behaviours of their subject matter or “target” in a
different medium, so as to allow manipulations of those behaviours’
variables that are analogous to operations on the target proper. The
purpose of flight simulators for example lies in giving pilots a realistic impression of experience of flying an airplane. Events within the
flight simulation call for operations on the simulation’s controls that
are, in their effects on that simulation, analogous to the effects of the
same operations in the flight that is being simulated. The physical
or functional structure of an airplane will not have to be reproduced
for this purpose, nor, of course, the physical effects of handling or
mishandling an in-flight routine. Only an instructive simile thereof is
required. I hope to have shown that this situation is similar to what
we encounter in the TT, as originally conceived. No functional analogy between simulation and target is required at all, while the choice
and systematic role of observable similarities is contingent on the
didactic purpose of the simulation.
An investigative simulation, on the other hand, aims at reproducing a selection of the behaviours of the target system in a fashion that
allows for, or contributes to, an explanation of that behaviours’ effects. In a subset of cases, the explanation of the target’s functions is
included, too. Here, a faithful mapping of the variables of the simulation’s behaviours, and their transformations, upon the variables and
transformations on the target’s side is of paramount importance. No
phenomenal similarity is required, and a mere analogy of effects is
not sufficient, as that analogy might be coincidental. Instead, some
aspects of the internal, causal or functional, structure of the target
system will need to be systematically grasped. To this purpose, an
investigative simulation is guided by a theory concerning the target
system, while the range of its behaviours is not exhausted by that theory: Novel empirical insights are supposed to grow from such simulations, in a manner partly analogous to experimental practice.7 I
hope to have shown that this is what the TT might seem to aim at,
but does not achieve, as there is no underlying theory of the cognitive traits that appear to be simulated by proxy of imitating human
conversational behaviour.
An alternative proposal for an investigative role of the TT along
the lines suggested above would lie in creating analogues of some of
the cognitive functions of communicative behaviour. Doing so would
not necessarily require a detailed reproduction of all or even most underlying cognitive traits of human beings. Although such a reproduction would be a legitimate endeavour taken by itself, although probably a daunting one, it would remain confined to the same individualistic bias that marked Turing’s own approach. A less individualistic,
and perhaps more practicable approach might take supra-individual
patterns of communicative interaction and their functions rather than
individual minds as its target.
One function of human communication, it may be assumed, lies
in the co-ordination of actions directed at shared tasks. If this is so, a
modified TT-style simulation would aim at producing, in evolutionary fashion, ‘generations’ of communicative patterns to be tried and
tested in interaction with human counterparts. The general method
would be similar to evolutionary robotics,8 but, firstly, placed on a
higher level of behavioural complexity and, secondly, directly incorporating the behaviour of human communicators. In order to allow
for some such quasi-evolutionary process to occur, there should not
be a reward for the machine passing the TT, nor for the human counterpart revealing the machine’s nature. Instead, failures of the machine to effectively communicate with its human counterpart, in re7
8

6

For historical accounts of the Modern Synthesis, see, for example, [10, 6].

For this argument on the epistemic role of computer simulations, see [22].
For a paradigmatic description of the research programme of evolutionary
robotics, see [14].

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lation to a given task, would be punished by non-reproduction, in
the next ‘generation’, of the mechanism responsible for the communicative pattern, replacing it with a slightly (and perhaps randomly)
variant form of that mechanism. In this fashion, an adaptive function
could be established for the mechanism in question over the course
of time. Turing indeed hints at such a possibility when briefly discussing the “child machine” towards the end of [19, pp. 455–460] –
a discussion that, in his essay, appears somewhat detached form the
imitation game proper.
For such patterns to evolve, the setup of the TT as a game of imitation and deception might have to be left behind – if only because
imitation and deception, although certainly part of human communication, are not likely to constitute its foundation. Even on a fairly
pessimistic view of human nature, they are parasitic on the adaptive functions of communication, which are more likely to be cooperative.9 Under this provision, humans and machines would be endowed with the task of trying to solve a cognitive or practical problem in co-ordinated, perhaps collaborative, fashion. In such a situation, the machine intriguingly would neither be conceived of as an instrument of human problem-solving nor as an autonomous agent that
acts beyond human control. It would rather be embedded in a shared
environment of interaction and communication that poses one and
the same set of challenges to human and machine actors, with at least
partly similar conditions of success. If that success is best achieved in
an arrangement of symmetrical collaboration, the mechanisms of selection of behavioural patterns, the behavioural tasks and the price of
failure would be comparable between human beings and machines.
By means of this modified and repurposed TT, some of the functions of human communication could be systematically elucidated
by means of an investigative simulation. That simulation would establish functional analogies between human and machine behaviour
in quasi-evolutionary fashion.

5

Conclusion

It might look like an irony that, where, on the analysis presented in
this paper, the common ground that would have to be shared between
human beings and machines in order to indicate what cognitive traits
they may share, ultimately and in theory at least, is functionally identified, and where the author of that thought experiment contributed to
developing the notion of decoupling the function of a system from its
physical structure, the very notion of functional analogy did not enter that same author’s focus. As indicated in section 4 above, putting
the blame on Turing himself would be an act of historical injustice.
At the same instance however, my observations about the formalistic
and individualistic biases built into Turing’s computational method
do nothing to belittle the merits of that method as such, as its practical
implementations first allowed for devising computational models and
simulations of a variety of functional patterns in a different medium,
and as its theoretical implications invited systematical investigations
into the physical underdetermination of functions in general. In some
respects, it might have taken those biases to enter this realm in the
first place.

[3] The Essential Turing, ed., B. Jack Copeland, Oxford University Press,
Oxford, 2004.
[4] B. Jack Copeland, ‘The Church-Turing Thesis’, in The Stanford Encyclopedia of Philosophy, html, The Metaphysics Research Lab, Stanford, spring 2009 edn., (2009).
[5] Charles Darwin, On The Origin of Species by Means of Natural Selection. Or the Preservation of Favoured Races in the Struggle for Life,
John Murray, London, 1 edn., 1859.
[6] David J. Depew and Bruce H. Weber, Darwinism Evolving. Systems
Dynamics and the Genealogy of Natural Selection, MIT Press, Cambridge/London, 1995.
[7] Kurt Gödel, ‘Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I’, Monatshefte für Mathematik, 38,
173–198, (1931).
[8] David Hilbert and Wilhelm Ackermann, Grundzüge der theoretischen
Logik, J. Springer, Berlin, 1928.
[9] Andrew Hodges, ‘What Did Alan Turing Mean by “Machine”?’, in The
Mechanical Mind in History, eds., Philip Husbands, Owen Holland, and
Michael Wheeler, 75–90, MIT Press, Cambridge/London, (2008).
[10] Ernst Mayr, One Long Argument. Charles Darwin and the Genesis of
Modern Evolutionary Thought, Harvard University Press, Cambridge,
1991.
[11] Ruth Garrett Millikan, Language, Thought, and Other Biological Categories, MIT Press, Cambridge/London, 1984.
[12] Ruth Garrett Millikan, Varieties of Meaning, MIT Press, Cambridge/London, 2004.
[13] James H. Moor, ‘An Analysis of the Turing Test’, Philosophical Studies, 30, 249–257, (1976).
[14] Stefano Nolfi and Dario Floreano, Evolutionary Robotics: The Biology,
Intelligence and Technology of Self-Organizing Machines, MIT Press,
Cambridge/London, 2000.
[15] Richard Owen, On the Archetype and Homologies of the Vertebrate
Skeleton, John van Voorst, Lodon, 1848.
[16] Philosophical Perspectives in Artificial Intelligence, ed., Martin Ringle,
Humanities Press, Atlantic Highlands, 1979.
[17] D’Arcy Wentworth Thompson, On Growth and Form, Cambridge University Press, Cambridge, 2 edn., 1942.
[18] Alan M. Turing, ‘On Computable Numbers, with an Application to the
Entscheidungsproblem’, Proceedings of the London Mathematical Society, s2-42, 230–265, (1936).
[19] Alan M. Turing, ‘Computing Machinery and Intelligence’, Mind, 59,
433–460, (1950).
[20] Alan M. Turing, ‘The Chemical Basis of Morphogenesis’, Philosophical Transactions of the Royal Society, B, 237, 37–72, (1952).
[21] Blay Whitby, ‘The Turing Test: AI’s Biggest Blind Alley?’, in Machines and Thought, eds., Peter Millican and Andy Clark, volume 1
of The Legacy of Alan Turing, 53–62, Clarendon Press, Oxford, (1996).
[22] Eric B. Winsberg, Science in the Age of Computer Simulation, University of Chicago Press, Chicago, 2010.
[23] Larry Wright, ‘Functions’, Philosophical Review, 82, 139–168, (1973).

References
[1] Margaret A. Boden, Mind as Machine: A History of Cognitive Science,
Oxford University Press, Oxford, 2006.
[2] B. Jack Copeland, ‘The Turing Test’, Minds and Machines, 10, 519–
539, (2000).
9

For an account of the evolution of co-operative functions, see, for example,
[12, ch. 2].

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