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Scalable solution methods for Dec-POMDPs with deterministic dynamics

Abstract:

Many high-level multi-agent planning problems, such as multirobot navigation and path planning, can be modeled with deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs)—a subclass of DecPOMDPs with deterministic transitions and observations given the state and joint actions. We then propose a practical solver, Iterative Deterministic POMDP Planning (IDPP), based on the classic Joint Equilibrium Search for Policies framework, specifically optimized to handle large-scale Det-Dec-POMDPs that existing Dec-POMDP solvers cannot handle efficiently.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1609/aaai.v40i43.40972

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Kellogg College
Role:
Author
ORCID:
0000-0002-7597-5684
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author


Publisher:
Association for the Advancement of Artificial Intelligence
Host title:
Proceedings of the 40th AAAI Conference on Artificial Intelligence
Volume:
40
Issue:
43
Pages:
36500-36508
Publication date:
2026-03-14
Acceptance date:
2025-11-07
Event title:
40th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)
Event location:
Singapore
Event website:
https://aaai.org/conference/aaai/aaai-26/
Event start date:
2026-01-20
Event end date:
2026-01-27
DOI:
EISSN:
2374-3468
ISSN:
2159-5399
ISBN-10:
1577359062
ISBN-13:
9781577359067


Language:
English
Pubs id:
2320814
Local pid:
pubs:2320814
Deposit date:
2025-11-10
ARK identifier:

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