Autonomous learning of target decision strategies without communications for continuous coordinated cleaning tasks

Keisuke Yoneda, Chihiro Kato, Toshiharu Sugawara

研究成果: Conference contribution

12 被引用数 (Scopus)

抄録

We propose a method for the autonomous learning of target decision strategies for coordination in the continuous cleaning domain. With ongoing advances in computer and sensor technologies, we can expect robot applications for covering large areas that often require coordinated/cooperative activities by multiple robots. In this paper, we focus the cleaning tasks by multiple robots or by agents, software to control the robots. We assume that agents cannot directly exchange internal information such as plans and targets for coordination, but rather individually learn their target decision strategies by observing how much trash/dirt has been vacuumed up in the multi-agent system environments. We experimentally evaluated the proposed method by comparing its performance with those obtained by the regimes of agents with a single strategy. Results showed that the proposed method enables agents to select target decision strategies from their own perspectives, resulting in the appropriate combinations of multiple strategies.

本文言語English
ホスト出版物のタイトルProceedings - 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
出版社IEEE Computer Society
ページ216-223
ページ数8
ISBN(印刷版)9781479929023
DOI
出版ステータスPublished - 2013
イベント2013 12th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013 - Atlanta, GA, United States
継続期間: 2013 11 172013 11 20

出版物シリーズ

名前Proceedings - 2013 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
2

Conference

Conference2013 12th IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2013
国/地域United States
CityAtlanta, GA
Period13/11/1713/11/20

ASJC Scopus subject areas

  • 人工知能

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