Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2010

Abstract

Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up existing algorithms, largely due to the use of point-based methods. Performing point-based backup is a fundamental operation in state-of-the-art algorithms. We show that even a single backup step in the multi-agent setting is NP-Complete. Despite this negative worst-case result, we present an efficient and scalable optimal algorithm as well as a principled approximation scheme. The optimal algorithm exploits recent advances in the weighted CSP literature to overcome the complexity of the backup operation. The polytime approximation scheme provides a constant factor approximation guarantee based on the number of belief points. In experiments on standard domains, the optimal approach provides significant speedup (up to 2 orders of magnitude) over the previous best optimal algorithm and is able to increase the number of belief points by more than a factor of 3. The approximation scheme also works well in practice, providing near-optimal solutions to the backup problem.

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems AAMAS 2010, May 10-14, Toronto

Volume

1

First Page

1315

Last Page

1322

ISBN

9780982657119

Publisher

IFAAMAS

City or Country

Richland, SC

Copyright Owner and License

IFAAMAS

Additional URL

https://dl.acm.org/citation.cfm?id=1838378

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