  Decentralized POMDPs provide an expressive framework for multi-agent
sequential decision making. While fnite-horizon DECPOMDPs have enjoyed
signifcant success, progress remains slow for the infnite-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 infnite-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.
