Multi-agent systems performance by adaptive/non-adaptive agent selection

Toshiharu Sugawara, Kensuke Fukuda, Toshio Hirotsu, Shin Ya Sato, Satoshi Kurihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06
Pages555-559
Number of pages5
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06 - Hong Kong
Duration: 2006 Dec 182006 Dec 22

Other

Other2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06
CityHong Kong
Period06/12/1806/12/22

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Sugawara, T., Fukuda, K., Hirotsu, T., Sato, S. Y., & Kurihara, S. (2007). Multi-agent systems performance by adaptive/non-adaptive agent selection. In Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06 (pp. 555-559). [4052976] https://doi.org/10.1109/IAT.2006.93