Data mining and agent technology: A fruitful symbiosis

Abstract

Multi-agent systems (MAS) have grown quite popular in a wide spectrum of applications where argumentation, communication, scaling and adaptability are requested. And though the need for well-established engineering approaches for building and evaluating such intelligent systems has emerged, currently no widely accepted methodology exists, mainly due to lack of consensus on relevant definitions and scope of applicability. Even existing well-tested evaluation methodologies applied in traditional software engineering, prove inadequate to address the unpredictable emerging factors of the behavior of intelligent components. The following chapter aims to present such a unified and integrated methodology for a specific category of MAS. It takes all constraints and issues into account and denotes the way knowledge extracted with the use of Data mining (DM) techniques can be used for the formulation initially, and the improvement, in the long run, of agent reasoning and MAS performance. The coupling of DM and Agent Technology (AT) principles, proposed within the context of this chapter is therefore expected to provide to the reader an efficient gateway for developing and evaluating highly reconfigurable software approaches that incorporate domain knowledge and provide sophisticated Decision Making capabilities. The main objectives of this chapter could be summarized into the following: a) introduce Agent Technology (AT) as a successful paradigm for building Data Mining (DM)-enriched applications, b) provide a methodology for (re)evaluating the performance of such DM-enriched Multi-Agent Systems (MAS), c) Introduce Agent Academy II, an Agent-Oriented Software Engineering framework for building MAS that incorporate knowledge model extracted by the use of (classical and novel) DM techniques and d) denote the benefits of the proposed approach through a real-world demonstrator. This chapter provides a link between DM and AT and explains how these technologies can efficiently cooperate with each other. The exploitation of useful knowledge extracted by the use of DM may considerably improve agent infrastructures, while also increasing reusability and minimizing customization costs. The synergy between DM and AT is ultimately expected to provide MAS with higher levels of autonomy, adaptability and accuracy and, hence, intelligence. © 2008 Springer-Verlag US.