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

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

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06
出版社IEEE Computer Society
ページ555-559
ページ数5
ISBN(印刷版)9780769527482
DOI
出版ステータスPublished - 2006
イベント2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06 - Hong Kong, China
継続期間: 2006 12 182006 12 22

出版物シリーズ

名前Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06

Other

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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

フィンガープリント 「Multi-agent systems performance by adaptive/non-adaptive agent selection」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル