Advertisement

Modeling Minority Games with BDI Agents – A Case Study

  • Wolfgang Renz
  • Jan Sudeikat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3550)

Abstract

Binary decisions are common in our daily lives and often individuals can gain by choosing the minority’s side. The socio–economically inspired Minority Game (MG) has been introduced as an exact model of the famous El Farol’s Bar Problem, which exhibits complex behavior. In this paper we show that the MG players can be naturally modeled by agents using reactive planning, implemented with a common deliberative programming paradigm, the Belief–Desire–Intention (BDI) model. Our simulation framework is build in Jadex, a forthcoming platform implementing BDI notions. Straightforward implementation of multi–agent simulations is enabled by XML agent descriptions and referenced Java classes. Design of the player agents and simulation results are shown. As a case study, we introduce a new adaptive stochastic MG with dynamically evolving strategies. It exhibits different regimes, reaching from optimal cooperation to destructive behavior, including the emergence of the so called ”Schwarzer Peter” game, depending on control parameters. We identify optimization mechanisms like rotation in the working regime as well as metastable behavior.

Keywords

Multiagent System Requirement Engineer Reactive Planning Minority Game Strategy Case 
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.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Series in Articial Intelligence, No. 0-13-103805-2. Prentice Hall, Englewood Cliffs (1995)Google Scholar
  2. 2.
    Arthur, W.B.: Inductive reasoning and bounded rationality. American Economic Review 84, 406–411 (1994), available at, http://ideas.repec.org/a/aea/aecrev/v84y1994i2p406-11.html Google Scholar
  3. 3.
    Challet, D., Zhang, Y.C.: Emergence of cooperation and organization in an evolutionary game. Physica A 246, 407 (1997)CrossRefGoogle Scholar
  4. 4.
    Metzler, R., Horn, C.: Evolutionary minority games: the benefits of imitation. Physica A 329, 484–498 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Johnson, N., Hui, P., Jonson, R., Lo, T.: Self-organized segregation within an evolving population. Physical Review Letters 82, 3360 (1999)CrossRefGoogle Scholar
  6. 6.
    Reents, G., Metzler, R., Kinzel, W.: A stochastic strategy for the minority game. Physica A 299, 253–261 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Challet, D., Marsili, M., Zhang, Y.C.: Minority Games - Interacting agents in financial markets. Oxford Finance Series, No. 0-19-856640-9. Oxford University Press, Oxford (2004)Google Scholar
  8. 8.
    Challet, D.: Competition between adaptive agents: from learning to collective efficiency and back. In: chapter to appear in Collectives and the design of complex systems. cond-mat/0210319. Springer, Heidelberg (2003)Google Scholar
  9. 9.
    Shalizi, C.R.: Methods and techniques of complex systems science: An overview. Nonlinear Sciences, nlin.AO/0307015 (2003)Google Scholar
  10. 10.
    Parunak, H.V.D., Savit, R., Riolo, R.L.: Agent-based modeling vs. equation-based modeling: A case study and users’ guide. In: Sichman, J.S., Conte, R., Gilbert, N. (eds.) MABS 1998. LNCS (LNAI), vol. 1534, pp. 10–25. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Drogoul, A., Vanbergue, D., Meurisse, T.: Multi-agent based simulation: Where are the agents? In: Sichman, J.S., Bousquet, F., Davidsson, P. (eds.) MABS 2002. LNCS (LNAI), vol. 2581, pp. 1–15. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Jennings, N.R.: On agent-based software engineering. Artif. Intell. 117, 277–296 (2000)zbMATHCrossRefGoogle Scholar
  13. 13.
    Jennings, N.R.: Building complex, distributed systems: the case for an agent-based approach. Comms. of the ACM 44 (4), 35–41 (2001)CrossRefGoogle Scholar
  14. 14.
    Rao, A.S., Georgeff, M.P.: BDI-agents: from theory to practice. In: Proceedings of the First Intl. Conference on Multiagent Systems, San Francisco (1995)Google Scholar
  15. 15.
    Weiß, G.: Agent orientation in software engineering. Knowledge Engineering Review 16(4), 349–373 (2002)Google Scholar
  16. 16.
    Sudeikat, J., Braubach, L., Pokahr, A., Lamersdorf, W.: Evaluation of agent - oriented software methodologies - examination of the gap between modeling and platform. In: Odell, J.J., Giorgini, P., Müller, J.P. (eds.) AOSE 2004. LNCS, vol. 3382, pp. 126–141. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Luck, M., Preist, P.M.C.: Agent Technology: Enabling Next Generation Computing. Agentlink II (2003) ISBN 0854 327886Google Scholar
  18. 18.
    Parunak, H.V.D., Brueckner, S., Savit, R.: Universality in multi-agent systems. In: AAMAS 2004: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 930–937. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  19. 19.
    Georgeff, M.P., Lansky, A.L.: Reactive reasoning and planning: an experiment with a mobile robot. In: Proceedings of the 1987 National Conference on Artificial Intelligence (AAAI 1987), Seattle, Washington, pp. 677–682 (1987)Google Scholar
  20. 20.
    Bratman, M.E.: Intentions, Plans, and Practical Reason. Harvard Univ. Press, Cambridge (1987)Google Scholar
  21. 21.
    Rao, A.S.: Agentspeak(l): Bdi agents speak out in a logical computable language. In: Perram, J., Van de Velde, W. (eds.) MAAMAW 1996. LNCS, vol. 1038, pp. 42–55. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  22. 22.
    Braubach, L., Pokahr, A., Lamersdorf, W., Moldt, D.: Goal representation for bdi agent systems. In: Bordini, R.H., Dastani, M., Dix, J., Fallah-Seghrouchni, A.E. (eds.) Second International Workshop on Programming Multiagent Systems: Languages and Tools, pp. 9–20 (2004)Google Scholar
  23. 23.
    Braubach, L., Pokahr, A., Lamersdorf, W.: Jadex: A short overview. In: Main Conference Net.ObjectDays 2004, pp. 195–207 (2004)Google Scholar
  24. 24.
    Pokahr, A., Braubach, L., Lamersdorf, W.: Jadex: Implementing a bdi-infrastructure for jade agents. EXP - in search of innovation (Special Issue on JADE) 3, 76–85 (2003)Google Scholar
  25. 25.
    Bellifemine, F., Rimassa, G., Poggi, A.: Jade – a fipa-compliant agent framework. In: 4th International Conference on the Practical Applications of Agents and Multi-Agent Systems (PAAM 1999), London, UK, pp. 97–108 (1999)Google Scholar
  26. 26.
    van Lamsweerde, A.: Goal-oriented requirements engineering: A guided tour. In: Proc. RE 2001 - Int. Joint Conference on Requirements Engineering (2001)Google Scholar
  27. 27.
    Cavagna, A.: Irrelevance of memory in the minority game. Phys. Rev. E 59 (1999)Google Scholar
  28. 28.
    Burgos, E., Ceva, H.: Self organization in a minority game: the role of memory and a probabilistic approach. In: cond-mat/0003179 (2000)Google Scholar
  29. 29.
    Xie, Y., Wang, B.H., Hu, C., Zhou, T.: Global Optimization of Minority Game by Smart Agents. ArXiv Condensed Matter e-prints (2004)Google Scholar
  30. 30.
    Zhong, L.X., Zheng, D.F., Zheng, B., Hui, P.: Effects of contrarians in the minority game. In: cond-mat/0412524 (2004)Google Scholar
  31. 31.
    Challet, D., Marsili, M., Zhang, Y.C.: Modeling market mechanism with minority game. Physica A 276, 284 (2000) (preprint cond-mat/9909265)Google Scholar
  32. 32.
    Zhang, Y.C.: Toward a theory of marginally efficient markets. Physica A 269, 30 (1999) (eprint arXiv:cond-mat/9901243)Google Scholar
  33. 33.
    Bazzan, A.L., Bordini, R.H., Andrioti, G.K., Vicari, R.M.: Wayward agents in a commuting scenario (personalities in the minority game). In: Proc. of the Fourth Int. Conf. on Multi-Agent Systems (ICMAS 2000), Boston, IEEE Computer Science, Los Alamitos (2000)Google Scholar
  34. 34.
    Wooldridge, M., Jennings, N.: Software engineering with agents: Pitfalls and pratfalls. IEEE Internet Computing 3 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wolfgang Renz
    • 1
  • Jan Sudeikat
    • 1
  1. 1.Multimedia Systems LaboratoryHamburg University of Applied SciencesHamburgGermany

Personalised recommendations