Networked distributed POMDPs: A synthesis of distributed constraint optimization and POMDPs

Ranjit Nair, Pradeep Varakantham, Milind Tambe, Makoto Yokoo

研究成果: Contribution to conferencePaper査読

115 被引用数 (Scopus)

抄録

In many real-world multiagent applications such as distributed sensor nets, a network of agents is formed based on each agent's limited interactions with a small number of neighbors. While distributed POMDPs capture the real-world uncertainty in multiagent domains, they fail to exploit such locality of interaction. Distributed constraint optimization (DCOP) captures the locality of interaction but fails to capture planning under uncertainty. This paper present a new model synthesized from distributed POMDPs and DCOPs, called Networked Distributed POMDPs (ND-POMDPs). Exploiting network structure enables us to present two novel algorithms for ND-POMDPs: a distributed policy generation algorithm that performs local search and a systematic policy search that is guaranteed to reach the global optimal.

本文言語英語
ページ133-139
ページ数7
出版ステータス出版済み - 12 1 2005
イベント20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, 米国
継続期間: 7 9 20057 13 2005

その他

その他20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
国/地域米国
CityPittsburgh, PA
Period7/9/057/13/05

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人工知能

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