A Methodology for Predicting Agent Behavior by the Use of Data Mining Techniques

One of the most interesting issues in agent technology has always been the modeling and enhancement of agent behavior. Numerous approaches exist, attempting to optimally reflect both the inner states, as well as the perceived environment of an agent, in order to provide it either with reactivity or proactivity. Within the context of this paper, an alternative methodology for enhancing agent behavior is presented. The core feature of this methodology is that it exploits knowledge extracted by the use of data mining techniques on historical data, data that describe the actions of agents within the MAS they reside. The main issues related to the design, development, and evaluation of such a methodology for predicting agent actions are discussed, while the basic concessions made to enable agent cooperation are outlined. We also present κ-Profile, a new data mining mechanism for discovering action profiles and for providing recommendations on agent actions. Finally, indicative experimental results are apposed and discussed.

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