Scalable multiagent planning using probabilistic inference

  • Authors:

  • Akshat Kumar;Shlomo Zilberstein;Marc Toussaint

  • Affiliations:

  • Dept. of Computer Science, Univ. of Massachusetts Amherst;Dept. of Computer Science, Univ. of Massachusetts Amherst;Dept. of Mathematics and Computer Science, FU Berlin

  • Venue:
  • IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
  • Year:
  • 2011

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Abstract

Multiagent planning has seen much progress with the development of formal models such as Dec-POMDPs. 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-Maximization framework for likelihood maximization, we show that the necessary inference can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We derive a global update rule that combines these local inferences to monotonically increase the overall solution quality. Experiments on a large multiagent planning benchmark confirm the benefits of the new approach in terms of runtime and scalability.