Learning in a multi-agent system as a mean for effective resource management

Abstract

In this paper symbolic, supervised learning is used in a multiagent system for resource management. Environment is a Fish Bank game, where agents manage fishing companies. Rule induction is applied to generate ship allocation and cooperation rules. In this article system architecture and learning process are described and experimental results comparing performance of several types of agents are presented. The results obtained confirm that applying a supervised learning algorithm in a multi-agent system may improve resource management. © Springer-Verlag Berlin Heidelberg 2006.