Paper Info
Title
Genetic Fuzzy Systems To Evolve Interaction Strategies In Multiagent Systems
Abstract
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. (C) 2007 Wiley Periodicals, Inc.
Year
DOI
Venue
2007
10.1002/int.20234
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Field
DocType
Volume
Electricity market,Evolutionary algorithm,Multi-agent system,Exploit,Operator (computer programming),Artificial intelligence,Machine learning,Genetic fuzzy systems,Mathematics,Negotiation,Fuzzy rule
Journal
22
Issue
ISSN
Citations 
9
0884-8173
3
PageRank 
References 
Authors
0.40
7
2
Name
Order
Citations
PageRank
Igor Walter1152.85
Fernando A. C. Gomide25546.95