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A Comparison of Different Cognitive Paradigms Using Simple Animats in a Virtual Laboratory, with Implications to the Notion of Cognition

Gershenson, Carlos (2002) A Comparison of Different Cognitive Paradigms Using Simple Animats in a Virtual Laboratory, with Implications to the Notion of Cognition. [Thesis]

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Abstract

In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no best model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical aid, I note that there is no best approach for studying cognition, since different approaches have all advantages and disadvantages, because they study different aspects of cognition from different contexts. This has implications for current debates on proper approaches for cognition: all approaches are a bit proper, but none will be proper enough. I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition.

Item Type:Thesis
Keywords:notion of cognition, types of cognition, virtual laboratory, animats, cognitive architectures
Subjects:Biology > Animal Cognition
Philosophy > Philosophy of Mind
Computer Science > Artificial Intelligence
ID Code:2452
Deposited By:Gershenson, Carlos
Deposited On:05 Sep 2002
Last Modified:11 Mar 2011 08:55

References in Article

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29

Aerts, D. (2002). Being and change: foundations of a realistic operational formalism, in Aerts, D, M.

Czachor, and T. Durt (eds.) Probing the Structure of Quantum Mechanics: Nonlinearity,

Nonlocality, Probability and Axiomatics. World Scientific.

Ashby, W. R. (1947). The Nervous System as a Physical Machine: With Special Reference to the Origin

of Adaptive Behavior. Mind 56 (221), pp. 44-59.

Arbib, M. A. (1995). The Handbook of Brain Theory and Neural Networks. MIT Press.

Balkenius, C. and P. Gärdenfors (1991). Nonmonotonic Inferences in Neural Networks. In Allen, J.

A. et al. (eds.) Principles of Knowledge Representation and Reasoning: Proceedings of the Second

International Conference, pp. 32-39. Morgan Kaufmann.

Balkenius, C., J. Zlatev, C. Brezeal, K. Dautenhahn and H. Kozima (2001). (Eds.) Proceedings of the

First International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic

Systems. Lund University Cognitive Studies, vol. 85, Lund, Sweden.

Bar-Yam, Y. (1997). Dynamics of Complex Systems. Addison-Wesley.

Bekoff, M., C. Allen, and G. M. Burghardt (eds.) (2002). The Cognitive Animal : Empirical and

Theoretical Perspectives on Animal Cognition. MIT Press.

Beer, R. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive

behavior. In Maes, P., et. al. (Eds.), From animals to animats 4: Proceedings of the Fourth

International Conference on Simulation of Adaptive Behavior pp. 421-429. MIT Press.

Beer, R. D. (2000). Dynamical Approaches in Cognitive Science. Trends in Cognitive Neuroscience, 4

(3), pp. 91-99.

Berlekamp, E. R., J. H. Conway, and R. K. Guy (1982) What Is Life. Ch. 25 in Winning Ways for Your

Mathematical Plays, Vol. 2: Games in Particular. Academic Press.

Braitenberg, V. (1984). Vehicles: Experiments in Synthetic Psychology. MIT Press.

Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and

Automation. RA-2, April, pp. 14-23.

Brooks, R. A. (1991). Intelligence Without Reason. In Proceedings of the 12th International Joint

Conference on Artificial Intelligence. Morgan Kauffman.

Clark, A. (1997). Being There: Putting Brain, Body And World Together Again. MIT Press.

Clark, A. and C. Thornton (1997) Trading Spaces: Computation, Representation and the Limits of

Uninformed Learning. Behavioral and Brain Sciences, 20, pp. 57-90.

30

Clark, A. and J. Toribio (1995). Doing Without Representing? Synthese 101, pp. 401-431.

Di Paolo, E. A., J. Noble, and S. Bullock (2000). Simulation Models as Opaque Thought Experiments.

Artificial Life VII, pp. 1-6.

Fodor, J. A. (1976). The Language of Thought. Harvard University Press.

Fodor, J. A. and Z. W. Pylyshyn (1988). Connectionism and Cognitive Architecture: A Critical

Analysis. In Pinker, S. and J. Mehler (Eds.). Connections and Symbols. MIT Press.

Gärdenfors, P. (1994). How Logic Emerges from the Dynamics of Information. In van Eijck, J. and A.

Visser, Logic and Information Flow, pp. 49-77. MIT Press.

Gärdenfors, P. (2000). Conceptual Spaces. MIT Press.

Gershenson, C. (1998). Lógica multidimensional: un modelo de lógica paraconsistente. Memorias XI

Congreso Nacional ANIEI, pp. 132-141. Xalapa, México.

Gershenson, C. (1999). Modelling Emotions with Multidimensional Logic. Proceedings of the 18th

International Conference of the North American Fuzzy Information Processing Society (NAFIPS

‘99), pp. 42-46. New York City, NY.

Gershenson, C. (2001). Artificial Societies of Intelligent Agents. Unpublished BEng Thesis. Fundación

Arturo Rosenblueth, México.

Gershenson, C. (2002a). Complex Philosophy. Proceedings of the 1st Biennial Seminar on Philosophical,

Methodological & Epistemological Implications of Complexity Theory. La Habana, Cuba.

Gershenson, C. (2002b). Behaviour-based Knowledge Systems: An epigenetic path from Behaviour to

Knowledge. To appear in Proceedings of the 2nd Workshop on Epigenetic Robotics. Edinburgh.

Gershenson, C. (2002c). Classification of Random Boolean Networks. To appear in Artificial Life VIII.

Sydney, Australia.

Gershenson, C. (unpublished). Adaptive Development of Koncepts in Virtual Animats: Insights into

the Development of Knowledge. Adaptive Systems Project, COGS, University of Sussex, 2002.

Gershenson, C. and P. P. González, (2000). Dynamic Adjustment of the Motivation Degree in an

Action Selection Mechanism. Proceedings of ISA '2000. Wollongong, Australia.

Gershenson, C., P. P. González, and J. Negrete (2000). Thinking Adaptive: Towards a Behaviours

Virtual Laboratory. In Meyer, J. A. et. al. (Eds.), SAB2000 Proceedings Supplement. Paris,

France. ISAB Press.

González, P. P. (2000). Redes de Conductas Internas como Nodos-Pizarrón: Selección de Acciones y

Aprendizaje en un Robot Reactivo. PhD. Dissertation, Instituto de Investigaciones

Biomédicas/UNAM, México.

González, P. P., J. Negrete, A. J. Barreiro, and C. Gershenson (2000). A Model for Combination of

External and Internal Stimuli in the Action Selection of an Autonomous Agent. In Cairó, O.

et. al. MICAI 2000: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence

1793, pp. 621-633, Springer-Verlag.

Hallam, B., D. Floreano, J. Hallam, G. Hayes, and J.-A. Meyer (2002) (eds.). From Animals to Animats

7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior. MIT

Press.

Hershberg, U. and S. Efroni (2001). The immune system and other cognitive systems. Complexity 6 (5),

pp. 14-21.

Heylighen F. (1990) Autonomy and Cognition as the Maintenance and Processing of Distinctions, In:

Heylighen F., Rosseel E. & Demeyere F. (eds.), Self-Steering and Cognition in Complex Systems.

Gordon and Breach, pp. 89-106.

Holland, J. (1992). Adaptation in Natural and Artificial Systems. 2nd Ed. MIT Press.

31

Husbands, P., T. Smith, N. Jakobi, and M. O'Shea (1998). Better Living Through Chemistry: Evolving

GasNets for Robot Control, Connection Science, 10(4), pp. 185-210.

Jakobi, N., P. Husbands, and I. Harvey (1995). Noise and the reality gap: The use of simulation in

evolutionary robotics. In Advances in Artificial Life: Proceedings of the 3rd European Conference

on Artificial Life, pp. 704-720. Springer-Verlag, Lecture Notes in Artificial Intelligence 929.

Jonker, C. M., J. L. Snoep, J. Treur, H. V. Westerhoff, and W. C. A. Wijngaards. (2001). Embodied

Intentional Dynamics of Bacterial Behaviour. In: Pfeifer, R. and M. Lungarella (eds.),

Proceedings of the International Workshop on Emergence and Development of Embodied

Cognition.

Kauffman, S. A. (1969) Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets.

Journal of Theoretical Biology, 22, pp. 437-467.

Kirsch, D. (1991). Today the earwig, tomorrow man? Artificial Intelligence 47, pp 161-184.

Kolen, J. F. and J. B. Pollack (1995). The Observer’s Paradox: Apparent Computational Complexity

in Physical Systems. Journal of Experimental and Theoretical Artificial Intelligence, 7, pp. 253-

277.

Lambrinos, D. and Ch. Scheier (1995). Extended Braitenberg Architectures. Technical Report AI Lab

no. 95.10, Computer Science Department, University of Zurich.

Lenat, D. and E. Feigenbaum (1992). On the Thresholds of Knowledge. In Kirsh, D. (ed.) Foundations

of Artificial Intelligence. MIT Press.

Maes, P. (1990). Situated agents can have goals. Journal of Robotics and Autonomous Systems, 6 (1&2).

Maes, P. (1991). A bottom-up mechanism for behaviour selection in an artificial creature. In J. A.

Meyer and S.W. Wilson (eds.), From Animals to Animats: Proceedings of the First International

Conference on Simulation of Adaptive Behaviour. MIT Press/Bradford Books.

Maes, P. (1994). Modelling Adaptive Autonomous Agents. Journal of Artificial Life, 1 (1-2), MIT Press.

McClelland, J. L., D. E. Rumelhart and the PDP Research Group (Eds.) (1986). Parallel Distributed

Processing: Explorations in the Microstructure of Cognition, Vol. 2: Psychological and Biological

Models. MIT Press.

McFarland, D. (1981). The Oxford Companion to Animal Behavior. Oxford University Press.

Maturana, H. R. and F. J. Varela (1980). Autopoiesis and Cognition: The Realization of the Living.

Reidel.

Maturana, H. R. and F. J. Varela (1987). The Tree of Knowledge: The Biological Roots of Human

Understanding. Shambhala.

Newell, A. (1980). Physical Symbol Systems. Cognitive Science 4, pp. 135-183.

Newell, A. (1990). Unified Theories of Cognition. Harvard University Press.

Newell, A. and H. Simon (1972). Human Problem Solving. Prentice-Hall.

Peirce, C. S. (1932). Collected Papers of Charles Sanders Peirce, vol. 2: Elements of Logic. Hartshore, C.

and P. Weiss (eds.). Harvard University Press.

Piaget J. (1968). Genetic Epistemology. Columbia University Press.

Pylyshyn, Z. W. (1984). Computation and Cognition. MIT Press.

Riegler, A. (in press). When Is a Cognitive System Embodied? To appear in: Cognitive Systems

Research, special issue on “Situated and Embodied Cognition”.

Rosenblueth, A. and N. Wiener (1945). The role of models in science. Philosophy of Science, 12, pp.

316-321.

Rumelhart, D. E., J. L. McClelland, and the PDP Research Group (Eds.) (1986). Parallel Distributed

Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations. MIT Press.

Russell, B. and A. N. Whitehead (1910-13). Principia Mathematica. Cambridge University Press.

Seth, A. K. (1998). Evolving Action Selection and Selective Attention Without Actions, Attention, or

Selection. In Pfeifer, R., et. al. (Eds.), From animals to animats 5: Proceedings of the Fifth

International Conference on Simulation of Adaptive Behavior, pp. 139-147. MIT Press.

Shortliffe, E. (1976). Computer Based Medical Consultations: MYCIN. Elsevier.

Slocum, A. C., D. C. Downey, and R. D. Beer (2000). Further experiments in the evolution of

minimally cognitive behavior: From perceiving affordances to selective attention. In Meyer, J.-

A., et. al. (Eds.), From Animals to Animats 6: Proceedings of the Sixth International Conference

on Simulation of Adaptive Behavior, pp. 430-439. MIT Press.

Smolensky, P. (1988). On the Proper Treatment of Connectionism. Behavioural and Brain Sciences 11,

pp. 1-23.

Stewart, J. (1996). Cognition = life: Implications for higher-level cognition. Behavioural Processes 35,

(1-3) pp. 311-326.

Thornton, C. (2000). Truth From Trash: How Learning Makes Sense, MIT Press.

Turchin, V. (1977): The Phenomenon of Science. A cybernetic approach to human evolution. Columbia

University Press.

Turing, A. M. (1936-7). On Computable Numbers, with an Application to the Entscheidungsproblem.

Proc. London Math. Soc. (2), 42, pp. 230-265.

Tyrrell, T. (1993). Computational Mechanisms for Action Selection. PhD. Dissertation. University of

Edinburgh.

Varela, F., E. Thompson, and E. Rosch (1991). The Embodied Mind: Cognitive Science and Human

Experience. MIT Press.

Vauclair, J. (1996). Animal Cognition. Harvard University Press.

Walter, W. G. (1950). An Imitation of Life. Scientific American 182 (5), pp. 42-45.

Walter, W. G. (1951). A Machine That Learns. Scientific American 185 (2), pp. 60-63.

Webb, B. (2001). Can Robots Make Good Models of Biological Behaviour? Behavioral and Brain

Sciences 26 (6).

Wiener, N. (1948). Cybernetics; or, Control and Communication in the Animal and the Machine. MIT

Press.

Wolfram, S. (2002). A New Kind Of Science. Wolfram Media.

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