DCOPs meet the realworld: Exploring unknown reward matrices with applications to mobile sensor networks

Manish Jain, Matthew Taylor, Milind Tambe, Makoto Yokoo

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

39 被引用数 (Scopus)

抄録

Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.

本文言語英語
ホスト出版物のタイトルIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
出版社International Joint Conferences on Artificial Intelligence
ページ181-186
ページ数6
ISBN(印刷版)9781577354260
出版ステータス出版済み - 1 1 2009
イベント21st International Joint Conference on Artificial Intelligence, IJCAI 2009 - Pasadena, 米国
継続期間: 7 11 20097 16 2009

出版物シリーズ

名前IJCAI International Joint Conference on Artificial Intelligence
ISSN(印刷版)1045-0823

会議

会議21st International Joint Conference on Artificial Intelligence, IJCAI 2009
国/地域米国
CityPasadena
Period7/11/097/16/09

All Science Journal Classification (ASJC) codes

  • 人工知能

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