Multi-Agent Systems Performance by Adaptive/Non-Adaptive Agent Selection

Authors:
Toshiharu Sugawara;Kensuke Fukuda;Toshio Hirotsu;Shin-ya Sato;Satoshi Kurihara
Affiliations:
NTT Communication Science Labs.;National Institute of Informatics, Japan;Toyohashi University of Technology, Japan;NTT Network Innovation Labs;Osaka University, Japan
Venue:
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Year:
2006

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Abstract

Our research interest lies in studing how local strategies about partner agent selection using reinforcement learning with variable exploitation-versus-exploration parameters influence the overall efficiency of multi-agent systems (MAS). An agent often has to select appropriate agents to assign tasks that are not locally executable. Unfortunately no agent in an open environment can understand the all states of all agents, so this selection must be done according to local information. In this paper we investigate how the overall performance of MAS is affected by their individual learning parameters for adaptive partner selections for collaboration. We show experimental results using simulation and discuss why the overall performance of MAS varies.