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.
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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
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DOI: https://doi.org/10.1007/3-540-44640-0_13
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