Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Xia Qu, Prashant Doshi


This paper provides the first formalization of self-interested planning in multiagent settings using expectation-maximization (EM). Our formalization in the context of infinite-horizon and finitely-nested interactive POMDPs (I-POMDP) is distinct from EM formulations for POMDPs and cooperative multiagent planning frameworks. We exploit the graphical model structure specific to I-POMDPs, and present a new approach based on block-coordinate descent for further speed up. Forward filtering-backward sampling -- a combination of exact filtering with sampling -- is explored to exploit problem structure.