Abstract
Although multi-agent simulations are an intuitive way of conceptualizing systems that consist of autonomous actors, a major problem is the actual design of the agent behavior. In this contribution, we examine the potential of using agent-based learning for implementing the agent behavior. We enhanced SeSAm, a platform for agent-based simulation, by replacing the usual rule-based agent architecture by XCS, a well-known learning classifier system (LCS). The resulting model is tested using a simple evacuation scenario. The results show that on the one hand side plausible agent behavior could be learned. On the other hand side, though, the results are quite brittle concerning the frame of environmental feedback, perception and action modeling.
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
Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Waterman, D.A., Hayes-Roth, F. (eds.) Pattern directed inference systems, pp. 313–329. Academic Press, New York (1978)
Butz, M.V.: Combining gradient-basedwith evolutionary online learning: An introduction to learning classifier systems. In: 7th International Conference on Hybrid Intelligent Systems HIS 2007, pp. 12–17 (2007)
Klügl, F., Rindsfüser, G.: Large-scale agent-based pedestrian simulation. In: Petta, P., Müller, J.P., Klusch, M., Georgeff, M. (eds.) MATES 2007. LNCS (LNAI), vol. 4687, pp. 145–156. Springer, Heidelberg (2007)
Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)
Weiss, G.: Adaptation and learning in multiagent systems: Some remarks and a bibliography. In: Weiss, G., Sen, S. (eds.) Adaption and learning in multi-agent systems. Springer, Heidelberg (1996)
Klügl, F., Tuys, K., Sen, S. (eds.): ALAMAS&ALAg - Adaptive and Learning Agents and Multiagent Systems (workshop at AAMAS 2008) (2008)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)
Nowe, A., Verbeeck, K., Peeters, M.: Learning automata as a basis for multi agent reinforcement learning. In: Tuyls, K., ’t Hoen, P.J., Verbeeck, K., Sen, S. (eds.) LAMAS 2005. LNCS (LNAI), vol. 3898, pp. 71–85. Springer, Heidelberg (2006)
Oechslein, C., Hörnlein, A., Klügl, F.: Evolutionary optimization of societies in simulated multi-agent systems. In: Jonker, C., Letia, A., Lindemann, G., Uthmann, T. (eds.) MASHO Workshop at ECAI 2000, Humboldt-Universität, Berlin, vol. 149 (2000)
Smith, D.C., Cypher, A., Spohrer, J.: Kidsim: Programming agents without a programming language. Communications of the ACM 37(7), 54–67 (1994)
Adami, C.: Introduction to Artificial Life. Springer, Heidelberg (1998)
Grefenstette, J.J.: The Evolution of Strategies for Multi-agent Environments. Adaptive Behavior 1, 65–89 (1992)
Collins, R.J., Jefferson, D.R.: Antfarm: Towards simulated evolution. In: Langton, C.G., Taylor, C., Farmer, J.D., Rasmussen, S. (eds.) Artificial Life II, pp. 579–601. Addison-Wesley, Redwood City (1992)
Denzinger, J., Fuchs, M.: Experiments in learning prototypical situations for variants of the pursuit game. In: Proc. of Int. Conf. on Multi-Agent Systems, 1996, Kyoto 1996, pp. 48–55 (1996)
Guessoum, Z., Rejeb, L., Durand, R.: Using adaptive multi-agent systems to simulate economic models. In: AAMAS 2004, pp. 68–75. IEEE Computer Society, Los Alamitos (2004)
Lanzi, P.L.: Learning classifier systems from a reinforcement learning perspective. Soft Computing: A Fusion of Foundations, Methodologies and Applications 6, 162–170 (2002)
Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. (1992). University of Michigan Press (1975)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Orriols-Puig, A., Bernadó-Mansilla, E.: Bounding XCS’s parameters for unbalanced datasets. In: GECCO 2006: Genetic and Evolutionary Computation Conference, pp. 1561–1568 (2006)
Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design. Springer, Berlin (2006)
Butz, M.V.: XCSJava 1.0: An implementation of the XCS classifier system in Java. IlliGAL report 2000027, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Klügl, F., Hatko, R., Butz, M.V. (2008). Agent Learning Instead of Behavior Implementation for Simulations – A Case Study Using Classifier Systems. In: Bergmann, R., Lindemann, G., Kirn, S., Pěchouček, M. (eds) Multiagent System Technologies. MATES 2008. Lecture Notes in Computer Science(), vol 5244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87805-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-87805-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87804-9
Online ISBN: 978-3-540-87805-6
eBook Packages: Computer ScienceComputer Science (R0)