Advertisement

Fuzzy Rule-Based Strategy for a Market Selection Game

  • H. Ishibuchi
  • T. Nakashima
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 62)

Abstract

In this chapter, we describe how fuzzy rule-based systems can be applied to a market selection game with many players (e.g., 100 players) and several markets (e.g., five markets). Our market selection game is a non-cooperative repeated game where every player is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It is assumed that the market price of the product is determined by a linear function of the total supply at each market. For example, if many players choose a particular market to sell their products, the market price at that market is low. On the other hand, the market price is high if only a small number of players choose that market. In this manner, the market price at each market is determined by the actions of all players. In our market selection game, the point is to choose a market with a high market price, i.e., a market that is not chosen by many other players. In this chapter, we explain how fuzzy rule-based systems can be automatically trained for choosing an appropriate market through the iterative execution of our market selection game.

Keywords

Market Price Transportation Cost High Payoff Average Payoff Previous Action 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Axelrod, R. (1984), The Evolution of Cooperation, Basic Books, New York.Google Scholar
  2. [2]
    Axelrod, R. (1987), “The evolution of strategies in the iterated prisoner’s dilemma,” in Davis, L. (Ed.), Genetic Algorithms and Simulated Annealing, Morgan Kaufmann, Los Altos, pp. 32–41.Google Scholar
  3. [3]
    Fogel, D.B. (Ed.) (1996), Special Issue on the Prisoner’s Dilemma, BioSystems, vol. 37, pp. 1–176.Google Scholar
  4. [4]
    Romp, G. (1997), Game Theory: Introduction and Applications, Oxford University Press, New York.Google Scholar
  5. [5]
    Ishibuchi, H., Nakashima, T., Miyamoto, H., and Oh, C.H. (1997), “Fuzzy Q-learning for a multi-player non-cooperative repeated game,” Proceedings of 1997 IEEE International Conference on Fuzzy Systems, Barcelona, Spain, pp. 1573–1579.Google Scholar
  6. [6]
    Ishibuchi, H., Oh, C.H., and Nakashima, T. (1999), “Competition between strategies for a market selection game,” Complexity International, http: //www. csu. edu.au/ci/, vol. 6.
  7. [7]
    Watkins, C.J.C.H. and Dayan, P. (1992), “Q-learning,” Machine Learning, vol. 8, pp. 279–292.MATHGoogle Scholar
  8. [8]
    Glorennec, P.Y. (1994), “Fuzzy Q-leaming and dynamical fuzzy Q-learning,” Proceedings of the Third IEEE International Conference on Fuzzy Systems, Orlando, U.S.A., pp. 474–479.Google Scholar
  9. [9]
    Jouffe, L. and Glorennec, P.Y. (1996), “Comparison between connectionist and fuzzy Q-learning,” Proceedings of the Fourth International Conference on Soft Computing, Iizuka, Japan, pp. 557560.Google Scholar
  10. [10]
    Sugeno, M. (1985), “An introductory survey of fuzzy control,” Information Sciences, vol. 36, pp. 59–83.MathSciNetMATHCrossRefGoogle Scholar
  11. [11]
    Lee, C.C. (1990), “Fuzzy logic in control systems: fuzzy logic controller part I and part II,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 20, pp. 404–435.MATHCrossRefGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • H. Ishibuchi
  • T. Nakashima

There are no affiliations available

Personalised recommendations