Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2010

Abstract

Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While finite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infinite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infinite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) algorithm to optimize the joint policy represented as DBNs. Experiments on benchmark domains show that EM compares favorably against the state-of-the-art solvers.

Discipline

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

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the Twenty-Sixth Conference Conference on Uncertainty in Artificial Intelligence 2010, July 8-11, Catalina Island, CA

First Page

294

Last Page

301

ISBN

9780974903965

Publisher

AUAI Press

City or Country

Corvallis, OR

Additional URL

https://dslpitt.org/uai/papers/10/p294-kumar.pdf

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