Learning in a Multi-agent System as a Mean for Effective Resource Management

  • Bartłomiej Śnieżyński
  • Jarosław Koźlak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


In this paper symbolic, supervised learning is used in a multi-agent 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.


Allocation Strategy Multiagent System Cooperative Learning Learning Agent Supervise Learning Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bartłomiej Śnieżyński
    • 1
  • Jarosław Koźlak
    • 1
  1. 1.Institute of Computer ScienceAGH University of Science and TechnologyKrakówPoland

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