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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Śnieżyński, B., Koźlak, J. (2005). Learning in a Multi-agent Approach to a Fish Bank Game. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds) Multi-Agent Systems and Applications IV. CEEMAS 2005. Lecture Notes in Computer Science(), vol 3690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559221_62
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DOI: https://doi.org/10.1007/11559221_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29046-9
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