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Genetic fuzzy systems to evolve interaction strategies in multiagent systems

✍ Scribed by Igor Walter; Fernando Gomide

Publisher: John Wiley and Sons
Year: 2007
Tongue: English
Weight: 276 KB
Volume: 22
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.20234

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✦ Synopsis

This article suggests an evolutionary approach to designing interaction strategies for multiagent systems, focusing on strategies modeled as fuzzy rule-based systems. The aim is to learn models evolving database and rule bases to improve agent performance when playing in a competitive environment. In competitive situations, data for learning and tuning are rare, and rule bases must jointly evolve with the databases. We introduce an evolutionary algorithm whose operators use variable length chromosomes, a hierarchical relationship among individuals through fitness, and a scheme that successively explores and exploits the search space along generations. Evolution of interaction strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of negotiation mechanisms and their role as a coordination protocol. An application concerning an electricity market illustrates the effectiveness of the approach.

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