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Evolutionary Learning of Multiagents Using Strategic Coalition in the IPD Game

  • Seung-Ryong Yang
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2891)

Abstract

Social and economic systems consist of complex interactions among its members. Their behaviors become adaptive according to changing environment. In many cases, an individual’s behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner’s Dilemma (IPD) game, which is a simple model to deal with complex problems for dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results show that co-evolutionary learning with coalitions and confidence can produce better performing strategies that generalize well in dynamic environments.

Keywords

Generalization Ability Coalition Formation Coalition Structure Evolutionary Learn Game Form Coalition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ord, T., Blair, A.: Exploitation and peacekeeping: Introducing more sophisticated interactions to the iterated prisoner’s dilemma. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp. 1606–1611 (2002)Google Scholar
  2. 2.
    Tesfatsion, L.: Agent-based computational economics: Growing economics from the bottom up. Artificial Life 8, 55–82 (2002)CrossRefGoogle Scholar
  3. 3.
    Yao, X., Darwen, P.J.: An experimental study of N-person iterated prisoner’s dilemma games. Informatica 18, 435–450 (1994)zbMATHGoogle Scholar
  4. 4.
    Seo, Y.G., Cho, S.B., Yao, X.: Exploiting coalition in co-evolutionary learning. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1268–1275 (2000)Google Scholar
  5. 5.
    Fletcher, J.A., Zwick, M.: N-Player prisoner’s dilemma in multiple groups: A model of multilevel selection. In: Proceedings of the Artificial Life VII Workshops, Portland, Oregon (2000)Google Scholar
  6. 6.
    Seo, Y.G., Cho, S.B., Yao, X.: The impact of payoff function and local interaction on the N-player iterated prisoner’s dilemma. Knowledge and Information Systems: An International Journal 2(4), 461–478 (2000)zbMATHCrossRefGoogle Scholar
  7. 7.
    Darwen, P.J., Yao, X.: Speciation as automatic categorical modularization. IEEE Transactions on Evolutionary Computation 1(2), 101–108 (1997)CrossRefGoogle Scholar
  8. 8.
    Yao, X., Darwen, P.J.: How important is your reputation in a multi-agent environment. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC 1999), vol. 2, pp. II-575–II-580. IEEE Press, Piscataway (1999)Google Scholar
  9. 9.
    Darwen, P.J., Yao, X.: On evolving robust strategies for iterated prisoner’s dilemma. In: Yao, X. (ed.) AI-WS 1993 and 1994. LNCS, vol. 956, pp. 276–292. Springer, Heidelberg (1995)Google Scholar
  10. 10.
    Ashlock, D., Joenks, M.: ISAc lists, a different representation for program induction. In: Proceedings of the Third Annual Genetic Programming Conference on Genetic Programming 1998, pp. 3–10. Morgan Kaufmann, San Francisco (1998)Google Scholar
  11. 11.
    Axelrod, R.: The evolution of strategies in the iterated prisoner’s dilemma. In: Genetic Algorithms and Simulated Annealing, vol. 3, pp. 32–41. Morgan-Kaufmann, San Mateo (1987)Google Scholar
  12. 12.
    Shehory, O., Kraus, S.: Coalition formation among autonomous agents: Strategies and complexity. In: Müller, J.P., Castelfranchi, C. (eds.) MAAMAW 1993. LNCS, vol. 957, pp. 56–72. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  13. 13.
    Shehory, O., Sycara, K., Jha, S.: Multi-agent coordination through coalition formation. In: Rao, A., Singh, M.P., Wooldridge, M.J. (eds.) ATAL 1997. LNCS, vol. 1365, pp. 143–154. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Allsopp, D.N., Kirton, P., Bradshaw, M., et al.: Coalition agents experiment: Multiagent cooperation in international coalitions. IEEE Intelligent Systems 17, 26–35 (2002)Google Scholar
  15. 15.
    Tate, A., Bradshaw, M., Pechoucek, M.: Knowledge systems for coalition operations. IEEE Intelligent Systems 17, 14–16 (2002)Google Scholar
  16. 16.
    Sandholm, T.W., Lesser, V.R.: Coalitions among computationally bounded agents. Artificial Intelligence 94, 99–137 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Axelrod, R.: The Evolution of Cooperation. Basic Books, New York (1984)Google Scholar
  18. 18.
    Axelrod, R., Dion, D.: The further evolution of cooperation. Science 242, 1385–1390 (1988)CrossRefGoogle Scholar
  19. 19.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Seung-Ryong Yang
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

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