Abstract
This paper analyzes complex behaviors of a multi-agent system, which consists of interacting agents with evolutionally learning capabilities. The interaction and learning of agents are modeled using Connected Replicator Dynamics expanded from the evolutional game theory. The dynamic systems show various behavioral and decision changes the including bifurcation of chaos in physics. The main contributions of this paper are as follows: (1) In the multi-agent system, the emergence of chaotic behaviors is general and essential, although each agent does not have chaotic properties; (2) However, simple controlling agent with the KISS (Keep-It-Simple-Stupid) principle or a sheepdog agent domesticates the complex behavior.
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References
Axelrod, R.: The Complexity of Cooperation, pp. 5. Princeton (1997)
Axtell, R.L.: Why Agents? On The Varied Motivations for Agent Computing in the Social Sciences, working paper No.17, Center on Social and Economic Dynamics, The Brookings Inst (2000)
Hogg, T., Huberman, B.: Controlling Chaos in Distributed Systems. IEEE Transactions on Systems, Man, and Cybernetics 21(6), 1325–1332 (1991)
Ushio, T., Imamori, T., Yamasagi, T.: Controlling Chaos in Discrete-Time Computational Ecosystem. In: Chen (ed.) Controlling Chaos and Bifurcations in Engineering Systems, pp. 625–644. CRC Press, Boca Raton (2000)
Kunigami, M., Terano, T.: Behavior and Control in Replicator Dynamics as an Agents System (in Japanese). In: proceedings of Multi Agent and Cooperative Computation 2001, MACC2001 (2001) http://www-kasm.nii.ac.jp/macc2001-proceedings/MACC2001-24.pdf
Sato, Y., Crutchfield, J.P.: Coupled Replicator Equations for the Dynamics of Learning in Multiagent Systems, working paper of Santa Fe Institute (April 2002), http://www.santafe.edu/sfi/publications/Working-Papers/02-04-017.pdf
Hofbauer, J., Sigmund, K.: Evolutionary Games and Population Dynamics. Cambridge Univ. Press, Cambridge (1998)
Skyrms, B.: Chaos and the explanatory significance of equilibrium: Strange attractors in evolutionary game dynamics. In: Bicchieri, C., et al. (eds.) The Dynamics of Norms, pp. 199–222. Cambridge univ. press, Cambridge (1997)
Steeb, W.-H.: The Nonlinear Workbook, pp. 85–86. World Scientific Pub., Singapore (1999)
Deguchi, H.: Norm Game and Indirect Regulation of Multi agents Society. In: Computational Analysis of social and Organizational Systems: CASOS Conf. 2000, Carnegie Mellon, pp. 92–95 (2000)
Ott, E., Grebogi, C., Yorke, J.A.: Controlling Chaos. Physical Review Letters 64, 1196–1199 (1990)
Pyragas, K.: Continuous control of chaos by selfcontrolling feedback. Physics Letters A 170(6), 421–428 (1992)
Kittel, A., Parisi, J., Pyragas, K.: Delayed feedback control of chaos by self-adapting delay time. Physics Letters A 198, 433–436 (1995)
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Kunigami, M., Terano, T. (2003). Connected Replicator Dynamics and Their Control in a Learning Multi-agent System. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_3
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DOI: https://doi.org/10.1007/978-3-540-45080-1_3
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