Abstract
This paper develops a general model for evolutionary learning in agent-based modeling. The central concepts of the general model lie in internal model principle and mutual learning of agent’s internal models in an evolutionary way. This paper particularly presents network-type dynamic hypergame as a model to describe an evolutionary learning process in multi-agent situation and a simulation method by genetic algorithm to perform a network-type dynamic hypergame. The experimental results given in this paper show some requisite conditions to progress the learning process effectively.
This is a preview of subscription content, log in via an institution to check access.
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
R. Axelrod, “The Complexity of Cooperation,” Princeton University Press, 1997.
P.G. Bennet, “Hypergames: Developing a Model of Conflict,” Futures, Vol.12, pp.489–507, 1980.
L.E. Blume and D. Easley, “Learning to be Rational,” J. of Economic Theory, 26, 340–351, 1982.
K.M. Carley and L. Gasser, “Computational Organization Theory,” in G. Weiss (Ed.), Multiagent Systems — A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
R.C. Connant and W.R. Ashby, “Every good regulator of a system must be a model of that system,” Int.J.Systems Sc, 1(2), pp.89–97, 1970.
H. Dawid, “Adaptive Learning by Genetic Algorithms — Analytical Results and Applications to Economic Models,” 2nd Ed., Springer, 1999.
R. Espejo, W. Schuhmann, M. Schwaninger, and Bilello, U., “Organizational Transformation and Learning”, Wiley, 1996.
R.L. Flood and E.R. Carson, “Dealing with Complexity,” Plenum, 1988.
H. Hanappi, “Evolutionary Economics,” Avebury, 1994.
J.H. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor: University of Michigan Press, 1975, (2nd ed) MIT Press, 1992.
A. Marcet and T.J. Sargent, “Convergence of Least Squares learning Mechanisms in Self Referential Linear Stochastic Models,” J. of Economic Theory, 48, 337–368, 1989.
A. Okada, “Game Theory,” Yuhikaku Press, 1996 (in Japanese).
C. Patterson, “Evolution,” British Museum (Natural History), 1978.
U.S.Putro, K.Kijima and S.Takahashi, “Simulation of Adaptation Process in Hypergame Situation by Genetic Algorithm,” SAMS, (to appear).
U.S. Putro, K. Kijima and S. Takahashi, “Simulation Approach to Learning Problem in Hypergame Situation by Genetic Algorithm,” Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Vol.IV, pp.260–265, 1999.
U.S.Putro, K.Kijima and S.Takahashi, “Adaptive Learning of Hypergame Situations by Using Genetic Algorithm,” IEEE Trans, of Systems, Man and Cybernetics (to appear).
E. Rasmusen, “Games and Information,” Basil Blackwell, 1989.
S. Sen and Gerhard Weiss, “Learning in Multiagent Systems,” in G. Weiss (Ed.), Multiagent Systems — A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
J. Rosenhead (ed.), “Rational Analysis for a Problematic World — Problem Structuring Methods for Complexity, Uncertainty and Conflict,” Wiley, 1990.
S.Takahashi, “Evolutionary Approach to Hypergame Situation,” IEEE Trans, of Systems, Man and Cybernetics (submitted).
S. Takahashi, “Evolutionary Approach to Three-person Hypergame Situation,” Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Vol.IV, pp.254–259, 1999.
S. Takahashi, “General Morphism for Modeling Relations in Multimodeling,” Transactions of the Society for Computer Simulation International, Vol.13, No.4, pp.169–178, 1997.
S. Takahashi and Y. Takahara, “Logical Approach to Systems Theory,” Springer-Verlag, 1995.
S. Takahashi, B. Nakano and M. Arase, “Application of Genetic Algorithm to Analysis of Adaptation Processes of Individual Perceptions in Hypergame Situation,” J.of Japan Association of Management Information, Vol.4, No.1, 1995 (in Japanese).
F. Vega-Redondo, “Evolution, Games, and Economic Behaviour,” Oxford University Press, 1996.
J.W. Weibull, “Evolutionary Game Theory,” MIT Press, 1996.
M. Wooldridge, “Intelligent Agents,” in G. Weiss (Ed.), Multiagent Systems — A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
B.P. Zeigler, “Theory of Modelling and Simulation,” Wiley, 1976.
B.P. Zeigler, “Multi-Faceted Modelling and Discrete Event Simulation,” Academic Press, 1984.
B.P. Zeigler, “Object Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems, Academic Press,” 1990.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media New York
About this chapter
Cite this chapter
Takahashi, S. (2001). Evolutionary Learning in Agent-Based Modeling. In: Sarjoughian, H.S., Cellier, F.E. (eds) Discrete Event Modeling and Simulation Technologies. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3554-3_14
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3554-3_14
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2868-9
Online ISBN: 978-1-4757-3554-3
eBook Packages: Springer Book Archive