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Genetic fuzzy systems to evolve interaction strategies in multiagent systems
Author(s) -
Walter Igor,
Gomide Fernando
Publication year - 2007
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20234
Subject(s) - computer science , negotiation , artificial intelligence , exploit , fuzzy logic , multi agent system , scheme (mathematics) , fuzzy rule , protocol (science) , machine learning , theoretical computer science , fuzzy control system , mathematics , medicine , mathematical analysis , alternative medicine , computer security , pathology , political science , law
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. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 971–991, 2007.