Abstract
This paper describes experiments using multiple classifier system (CS) agents to play the iterated prisoner’s dilemma (IPD) under various conditions. Our main interest is in how, and under what circumstances, co-operation is most likely to emerge through competition between these agents. Experiments are conducted with agents playing fixed strategies and other agents individually and in tournaments, with differing CS parameters. Performance improves when reward is stored and averaged over longer periods, and when a genetic algorithm (GA) is used more frequently. Increasing the memory of the system improves performance to a point, but long memories proved difficult to reinforce fully and performed less well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. Axelrod. Effective choice in the prisoner’s dilemma. Journal of Conflict Resolution, 24:3–25, 1980.
R. Axelrod. More effective choice in the prisoner’s dilemma. Journal of Conflict Resolution, 24:379–403, 1980.
R. Axelrod. The evolution of strategies in the iterated prisoner’s dilemma. In L. Davis, editor, Genetic Algorithms and Simulated Annealing, pages 32–41. Morgan Kaufmann, 1987.
J. Bendor, R.M. Kramer, and S. Stout. When in doubt: Cooperation in a noisy prisoner’s dilema. Journal of Conflict Resolution, 35(4), 1991.
K.G. Binmore and L. Samuelson. Evolutionary stability in repeated games played by finite automata. Journal of Economic Theory, 57, 1992.
A. Carbonaro, G. Casadei, and A. Palareti. Genetic algorithms and classifier systems in simulating co-operative behaviour. In R.F. Albrecht and C.R. Reeves, C.R. Steele, editors, Artificial Neural Networks and Genetic Algorithms, pages 479–483. Springer-Verlag, Wien New York, 1993.
P.H. Crowley. Evolving cooperation: Strategies as hierachies of rules. BioSystems, 37:67–80, 1996.
G. Ellison. Learning, local interaction, and coordination. Econometra, 61(5), 1993.
A. Fairley and D.F. Yates. Improving simple classifier systems to alleviate the problems of duplication, subsumsion and equivalence of rules. In R.F. Albrecht, C.R Reeves, and N.C. Steele, editors, Artificial Neural Nets and Genetic Algorithms. Springer/Verlag, 1993.
D.B. Fogel. On the relationship between the duration of an encounter and the evolution of cooperation in the iterated prisoner’s dilemma. Evolutionary Computation, 3(3):349–363, 1996.
J.R. Hoffmann and N.C. Waring. The localisation of learning and interaction in the repeated prisoner’s dilemma. Draft Copy, University of East Anglia, 1996.
J.H. Holland. Processing and processors for scemata. In Jacks E.L., editor, Associative Information Processing, pages 127–146. American Elsevier, New York, 1971.
J.H. Holland. Properties of the bucket brigade algorithm. In Grefenstette J.J., editor, Proceedings of the First International Conference on Genetic Algorithms and Applications. Morgan Kaufmann, 1985.
J.H. Holland. The effect of labels (tags) on social interactions. Santa Fe Institute Discusion Paper 93-10-064, 1993.
O. Kirchamp. Spatial Evolution of Automata in the Prisoner’s Dilemma. PhD thesis, 1995.
Y. Mor, C.V. Goldman, and J.S. Rosenschein. Learn your opponents strategy (in polynomial time)! In: Adaptation and Learning in Multi-Agent Systems, (G. Weiss and S. Sen), pages 164–176, 1996.
J.F. Nash. Non-cooperative games. Annals of Mathematics, 54:286–295, 1951.
W. Poundstone. Prisoner’s Dilemma. Oxford University Press, 1993.
B.R. Routledge. Co-evolution and Spatial Interaction. PhD thesis, 1993.
A. Rubinstein. Finite automata in the repeated prisoner’s dilemma. Journal of Economic Theory, 39, 1986.
T.W. Sandholm and R.H. Crites. On multi-agent q-learning in a semi-competetive domain. In G. Weiss and S. Sen, editors, Adaptation and Learning in Multi-Agent Systems, pages 191–205. 1996.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Chalk, K., Smith, G.D. (1998). Multi-Agent Classifier Systems and the Iterated Prisoner’s Dilemma. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_136
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_136
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive