Agent Academy: An integrated tool for developing multi-agent systems and embedding decision structures into agents

By P.A. Mitkas, D.D. Kehagias, A.L. Symeonidis & I.N. Athanasiadis
In First European Workshop on Multi-Agent Systems (EUMAS 2003) , (M. d'Inverno, C. Sierra & F. Zambonelli, ed.) , Oxford, UK, 2003.

Abstract:
Cover image In this paper we present Agent Academy, a framework that enables software developers to quickly develop multi-agent applications, when prior historical data relevant to a desired rule-based behaviour are available. Agent Academy is implemented itself as a multi-agent system, that supports, in a single tool, the design of agent behaviours and reusable agent types, the definition of ontologies, and the instantiation of single agents or multi-agent communities. Once an agent has been designed within the framework, the agent developer can create a specific ontology that describes the historical data. In this way, agents become capable of having embedded rule-based reasoning. We call this procedure `agent training' and it is realized by the application of data mining and knowledge discovery techniques on the application-specific historical data. From this point of view, Agent Academy provides a tool for both creating multi-agent systems and embedding rule-based decision structures into one or more of the participating agents.

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