Adaptive agent selection in large-scale multi-agent systems

Authors:
Toshiharu Sugawara;Kensuke Fukuda;Toshio Hirotsu;Shin-Ya Sato;Satoshi Kurihara
Affiliations:
NTT Communication Science Laboratories, Atsugi, Kanagawa, Japan;National Institute of Informatics;Toyohashi University of Technology;NTT Network Innovation Laboratories;Osaka University
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 2
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Total performance by local agent selection strategies in multi-agent systems

AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Adaptive load balancing: a study in multi-agent learning

Journal of Artificial Intelligence Research

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Abstract

An agent in a multi-agent system (MAS) has to select appropriate agents to assign tasks. Unfortunately no agent in an open environment can identify the states of all agents, so this selection must be done according to local information about the other known agents; however this information is limited and may contain uncertainty. In this paper we investigate how overall performance of MAS is affected by learning parameters for adaptive strategies to select partner agent for collaboration. We show experimental results using simulation and discuss why overall performance of MAS varies.