Mining Patterns and Rules for Improving Agent Intelligence through an Integrated Multi-Agent Platform

A.L. Symeonidis, P.A. Mitkas, and D.D. Kechagias (Greece)


Data Mining, Multi-Agent Systems, Agent Training, Classification, Decision Trees, Association Rules


The integration of data mining techniques with multi-agent systems to assist in dealing with information overload has received some attention during the last years. Agent Academy, a platform for training agents, introduces a whole new perspective on the improvement of agent intelligence. Data mining techniques are used in order to extract useful patterns on real high-risk and time-efficient applications, and to provide the platform with rules, decisions and classes on test case data. These metadata are embedded into agents in order to improve their existing intelligence. This paper describes the Agent Academy platform and focuses on the issues and challenges its development has revealed through the prism of data mining.

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