Message-Passing Algorithms for Large Structured Decentralized POMDPs

• Main Authors: KUMAR, Akshat, ZILBERSTEIN, Shlomo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/2207; https://ink.library.smu.edu.sg/context/sis_research/article/3207/viewcontent/Message_Passing_Algorithms_for_Large_Structured_Decentralized_POMDPs__extended_abstract_.pdf
• Institution: Singapore Management University

Decentralized POMDPs provide a rigorous framework for multi-agent decision-theoretic planning. However, their high complexity has limited scalability. In this work, we present a promising new class of algorithms based on probabilistic inference for infinite-horizon ND-POMDPs---a restricted Dec-POMDP model. We first transform the policy optimization problem to that of likelihood maximization in a mixture of dynamic Bayes nets (DBNs). We then develop the Expectation-Maximization (EM) algorithm for maximizing the likelihood in this representation. The EM algorithm for ND-POMDPs lends itself naturally to a simple message-passing paradigm guided by the agent interaction graph. It is thus highly scalable w.r.t. the number of agents, can be easily parallelized, and produces good quality solutions.