Abstract
Classifier systems are rule-based systems dedicated to the learning of more or less complex tasks. They evolve toward a solution without any external help. When the problem is very intricate it is useful to have different systems, each of them being in charge with an easier part of the problem. The set of all the entities responsible for the resolution of each sub-task, forms a multi-agent system. Agents have to learn how to exchange information in order to solve the main problem. In this paper, we define the minimal requirements needed by a multi-agent classifier system to evolve communication. We thus design a minimal model involving two classifier systems which goal is to communicate with each other. A measure of entropy that evaluates the emergence of a common referent between agents has been finalised. Promising results let think that this work is only the beginning of our ongoing research activity.
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References
Bull, L., Carse, B., and Fogarty, T.C. (1995). Evolving Multi-Agent Systems. In Genetic Algorithms in Engineering and Computer Science, Periaux J. and Winter G., editors.
Holland, J.H. (1986). Escaping the brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In Mitchell, Michalski, and Carbonell (Ed.): Machine learning, an artificial intelligence approach, vol. II, chapter 20, pp. 593–623. Morgan Kaufmann.
Goldberg, D.E. (1989). Genetic algorithms in search, optimisation, and machine learning. Reading, MA: Addison-Wesley.
Wilson, S.W. (1995). Classifier fitness based on accuracy. Evolutionary Computation, 3(2), pp. 149–175.
Holland, J.H. (1985). Properties of the bucket brigade algorithm. Proceedings of the First International Conference on Genetic Algorithms and their Applications, pp. 1–7, Hillsdale, New Jersey: Lawrence Erlbaum Associates.
Smith, S.F. (1980). A learning system based on genetic adaptive algorithms. PhD. thesis. University of Pittsburgh.
Grefenstette, J.J. Ramsey, C. L., and Schultz, A. C. (1990). Learning sequential decision rules using simulation models and competition. Machine Learning, 5(4), pp. 357–381. Kluwer Academic Publishers.
Sudhir K. Rustogi and Munindar P. Singh (1999). Be Patient and Tolerate Imprecision: How Autonomous Agents can Coordinate Effectively. In IJCAI 1999, pp. 512–519. http://citeseer.nj.nec.com/237985.html
Luis Miramontes Hercog and Terence C. Fogarty (2000). XCS-based Inductive Multi-Agent System. Presented to IWLCS2000.
Arthur, B. (1994). Inductive Reasoning and Bounded Rationality. American Economic Association Papers, 84, 406–411.
Barbaroux, P. and Énée, G. (2000). Evolutionary Algorithms and Endogeneous Adaptive Learning: the “bar problem”. Submitted to the journal: Mind and Society.
Escazut, C. and Fogarty, T.C. (1997). Coevolving classifier systems to control traffic signals. In J.R. Koza (Ed.): ”Late breaking papers book”, Genetic Programming 1997 Conference.
Burghardt, G.M. (1970). Defining ”Communication”. In J.W. Johnston, Jr., D.G. Moulton, and A. Turk (Ed.): Communication by Chemical Signals, pp. 5–18. New York, NY: Appleton-Century-Crofts.
De Jong, K.A. (1975). An analysis of the behaviour of a class of genetic adaptive systems. PhD Thesis. University of Michigan.
Énée, G. and Escazut, C. (1999). Classifier Systems: Evolving multi-agent system with distributed elitism. Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1740–1746, IEEE Press, Washington D.C.
Steels, L. (1997). The synthetic modeling of language origins. Evolution of Communication Journal, 1(1), pp. 1–34.
Hinton, G. and Nowlan, S. (1987). How learning can guide evolution. Complex Systems, Vol. 1, pp. 495–502.
Shannon, C.E. and Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press, Urbana, Ill.
MacLennan, B.J. and Burghardt, G.M. (1994). Synthetic ethology and the evolution of cooperative communication. Adaptive Behavior, 2(2), Fall 1993, pp. 161–188. MIT Press.
MacLennan, B. (1999). The emergence of communication through synthetic evolution. To appear in Honavar, V. Patel, M., and Balakrishnan K. (Ed.): Advances in evolutionary synthesis of neural systems. MIT Press.
Michel, O. (1995). Khepera simulator homepage: http://diwww.epfl.ch/lami/team/michel/khep-sim/
Énée, G. and Escazut, C. (2001). A Minimal Model of Communication for Multi-Agent Systems. To appear in the proceedings of the 8th IEEE International Conference on Emerging Technologies and Factory Automation, Antibes France.
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Énée, G., Escazut, C. (2002). A Minimal Model of Communication for a Multi-agent Classifier System. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2001. Lecture Notes in Computer Science(), vol 2321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48104-4_3
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DOI: https://doi.org/10.1007/3-540-48104-4_3
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