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. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Śniezyński, B., & Koźlak, J. (2005). Learning in a multi-agent approach to a Fish Bank game. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3690 LNAI, pp. 568–571). Springer Verlag. https://doi.org/10.1007/11559221_62
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