Winning back the CUP for distributed POMDPs: Planning over continuous belief spaces

Pradeep Varakantham, Ranjit Nair, Milind Tambe, Makoto Yokoo

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

4 Citations (Scopus)

Abstract

Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling multiagent systems, and many different algorithms have been proposed to obtain locally or globally optimal policies. Unfortunately, most of these algorithms have either been explicitly designed or experimentally evaluated assuming knowledge of a starting belief point, an assumption that often does not hold in complex, uncertain domains. Instead, in such domains, it is important for agents to explicitly plan over continuous belief spaces. This paper provides a novel algorithm to explicitly compute finite horizon policies over continuous belief spaces, without restricting the space of policies. By marrying an efficient single-agent POMDP solver with a heuristic distributed POMDP policy-generation algorithm, locally optimal joint policies are obtained, each of which dominates within a different part of the belief region. We provide heuristics that significantly improve the efficiency of the resulting algorithm and provide detailed experimental results. To the best of our knowledge, these are the first run-time results for analytically generating policies over continuous belief spaces in distributed POMDPs.

Original languageEnglish
Title of host publicationProceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
Pages289-296
Number of pages8
DOIs
Publication statusPublished - 2006
EventFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS - Hakodate, Japan
Duration: May 8 2006May 12 2006

Publication series

NameProceedings of the International Conference on Autonomous Agents
Volume2006

Other

OtherFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
CountryJapan
CityHakodate
Period5/8/065/12/06

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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