Abstract
An Organizational-learning oriented Classifier System(OCS) is an extension of Learning Classifier Systems (LCSs) to multiagent environments, introducing the concepts of organizational learning (OL) in organization and management science. Unlike conventional research on LCSs which mainly focuses on single agent environments, OCS has an architecture for addressing multiagent environments. Through intensive experiments on a complex scalable domain, the following implications have been revealed: (1) OCS finds good solutions at small computa- tional costs in comparison with conventional LCSs, namely the Michigan and Pittsburgh approaches; (2) the learning mechanisms at the organi- zational level contribute to improving the performance in multiagent environments; (3) an estimation of environmental situations and utiliza- tion of records of past situations/actions must be implemented at the organizational level to cope with non-Markov properties in multiagent environments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
C. Argyris and D. A. Schön: Organizational Learning, Addison-Wesley, 1978.
L. Chirsman: “Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach”, The 10th National Conference on Artificial Intelligence (AAAI’ 92), pp. 183–188, 1992.
D. Cliff and S. Ross: “Adding Temporary Memory to ZCS”, Adaptive Behavior, Vol. 3, No. 2, pp. 101–150, 1995.
M. D. Cohen and L. S. Sproull: Organizational Learning, SAGE Publications, 1995.
K. Dejong: An analysis of the behavior of a class of genetic adaptive system, Ph.D. Thesis, University of Michigan, 1975.
D. E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
J. J. Grefenstette: “Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms,” Machine Learning, Vol. 3. pp. 225–245, 1988.
J. H. Holland and J. Reitman: “Cognitive Systems Based on Adaptive Algorithms,” in D. A. Waterman and F. Hayes-Roth (Eds.), Pattern Directed Inference System, Academic Press, 1978.
D. Kim: “The Link between individual and organizational learning,” Sloan Management Review, Fall, pp. 37–50, 1993.
P. L. Lanzi: “Adding Memory to XCS”, IEEE International Conference on Evolutionary Computation (ICEC’ 98), pp. 609–614, 1998.
P. L. Lanzi and S. W. Wilson: “Optimal Classifier System Performance in Non-Markov Environments”, technical report N.99.36, Dipartimento di Elettronicae Informazione, 1999.
R. A. MacCallum: “Overcoming Incomplete Perception with Utile Distinction Memory”, The 10th International Conference on Machine Learning (ICML’ 93), pp. 190–196, 1993.
S. J. Russell and P. Norving: Artificial Intelligence: A Modern Approach, Prentice-Hall International, 1995.
S. F. Smith: “Flexible learning of problem solving heuristics through adaptive search,” 1983 International Joint Conference on Artificial Intelligence (IJCAI’ 83), pp. 422–425, 1983.
R. S. Sutton, A. G. Bart: Reinforcement Learning — An Introduction-, The MIT Press, 1998.
K. Takadama, S. Nakasuka, and T. Terano: “Multiagent Reinforcement Learning with Organizational-Learning Oriented Classifier System,” IEEE 1998 International Conference On Evolutionary Computation (ICEC’ 98), pp. 63–68, 1998.
K. Takadama, T. Terano, K. Shimohara, K. Hori, and S. Nakasuka: “Making Organizational Learning Operational: Implications from Learning Classifier System”, Computational and Mathematical Organization Theory (CMOT), Kluwer Academic Publishers, Vol. 5, No. 3, pp. 229–252, 1999.
G. Weiss: Multiagent Systems — Modern Approach to Distributed Artificial Intelligence, The MIT Press, 1999.
S. W. Wilson: “ZCS: A Zeroth Level Classifier System”, Evolutionary Computation, Vol. 2, No. 1, pp. 1–18, 1994.
S. W. Wilson: “Classifier Fitness Based on Accuracy”, Evolutionary Computation, Vol. 3, No. 2, pp. 149–175, 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takadama, K., Terano, T., Shimohara, K. (2001). Learning Classifier Systems Meet Multiagent Environments. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2000. Lecture Notes in Computer Science(), vol 1996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44640-0_13
Download citation
DOI: https://doi.org/10.1007/3-540-44640-0_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42437-6
Online ISBN: 978-3-540-44640-8
eBook Packages: Springer Book Archive