Scalable Multiagent Planning Using Probabilistic Inference

  title={Scalable Multiagent Planning Using Probabilistic Inference},
  author={Akshat Kumar and Shlomo Zilberstein and Marc Toussaint},
Multiagent planning has seen much progress with the development of formal models such as DecPOMDPs. However, the complexity of these models—NEXP-Complete even for two agents— has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical model in which likelihood maximization is equivalent to plan optimization. Using the Expectation… CONTINUE READING
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