Learning in a multi-agent approach to a fish bank game

Authors:
Bartłomiej Śnieżyński;Jarosław Koźlak
Affiliations:
Institute of Computer Science, AGH University of Science and Technology, Kraków, Poland;Institute of Computer Science, AGH University of Science and Technology, Kraków, Poland
Venue:
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Year:
2005

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Abstract

In this paper application of symbolic, supervised learning in a multi-agent system is presented. As an environment Fish Bank game is used. Agents represent players that manage fishing companies. Rule induction algorithm is applied to generate ship allocation rules. In this article system architecture and learning process are described and preliminary experimental results are presented. Results show that learning agent performance increases significantly when new experience is taken into account.