Evaluation of Techniques for a Learning-Driven Modeling Methodology in Multiagent Simulation

  • Robert Junges
  • Franziska Klügl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6251)

Abstract

There have been a number of suggestions for methodologies supporting the development of multiagent simulation models. In this contribution we are introducing a learning-driven methodology that exploits learning techniques for generating suggestions for agent behavior models based on a given environmental model. The output must be human-interpretable. We compare different candidates for learning techniques – classifier systems, neural networks and reinforcement learning – concerning their appropriateness for such a modeling methodology.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  2. 2.
    Nowe, A., Verbeeck, K., Peeters, M.: Learning automata as a basis for multi agent reinforcement learning, pp. 71–85 (2006)Google Scholar
  3. 3.
    Adami, C.: Introduction to artificial life. Springer, New York (1998)CrossRefMATHGoogle Scholar
  4. 4.
    Collins, R.J., Jefferson, D.R.: Antfarm: Towards simulated evolution. In: Artificial Life II, pp. 579–601. Addison-Wesley, Reading (1991)Google Scholar
  5. 5.
    Denzinger, J., Fuchs, M.: Experiments in learning prototypical situations for variants of the pursuit game. In: Proceedings on the International Conference on Multi-Agent Systems (ICMAS-1996), pp. 48–55. MIT Press, Cambridge (1995)Google Scholar
  6. 6.
    Maeda, Y.: Simulation for behavior learning of multi-agent robot. Journal of Intelligent and Fuzzy Systems, 53–64 (1998)Google Scholar
  7. 7.
    Mahadevan, S., Connell, J.: Automatic programming of behavior-based robots using reinforcement learning. Artificial Intelligence 55(2-3), 311–365 (1992)CrossRefGoogle Scholar
  8. 8.
    Lee, M.R., Kang, E.K.: Learning enabled cooperative agent behavior in an evolutionary and competitive environment. Neural Computing & Applications 15, 124–135 (2006)CrossRefGoogle Scholar
  9. 9.
    Neruda, R., Slusny, S., Vidnerova, P.: Performance comparison of relational reinforcement learning and rbf neural networks for small mobile robots. In: Proceedings of FGCNS ’08, Washington, DC, USA, pp. 29–32. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  10. 10.
    Klügl, F.: Multiagent simulation model design strategies. In: MAS& S Workshop at MALLOW 2009, CEUR Workshop Proceedings, Turin, Italy, vol. 494 (September 2009)Google Scholar
  11. 11.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar
  12. 12.
    Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine Learning 8(3), 279–292 (1992)MATHGoogle Scholar
  13. 13.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  14. 14.
    Klügl, F., Hatko, R., Butz, M.V.: Agent learning instead of behavior implementation for simulations - a case study using classifier systems. In: Bergmann, R., Lindemann, G., Kirn, S., Pěchouček, M. (eds.) MATES 2008. LNCS (LNAI), vol. 5244, pp. 111–122. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Butz, M.V.: XCSJava 1.0: An implementation of the XCS classifier system in Java. Illigal report, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robert Junges
    • 1
  • Franziska Klügl
    • 1
  1. 1.Modeling and Simulation Research CenterÖrebro UniversitySweden

Personalised recommendations