Scaling up optimal heuristic search in Dec-POMDPs via incremental expansion

  • Authors:

  • Matthijs T. J. Spaan;Frans A. Oliehoek;Christopher Amato

  • Affiliations:

  • Inst. for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal;CSAIL, Massachusetts Inst. of Technology, Cambridge, MA;Aptima, Inc., Woburn, MA

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

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Abstract

Planning under uncertainty for multiagent systems can be formalized as a decentralized partially observable Markov decision process. We advance the state of the art for optimal solution of this model, building on the Multiagent A* heuristic search method. A key insight is that we can avoid the full expansion of a search node that generates a number of children that is doubly exponential in the node's depth. Instead, we incrementally expand the children only when a next child might have the highest heuristic value. We target a subsequent bottleneck by introducing a more memory-efficient representation for our heuristic functions. Proof is given that the resulting algorithm is correct and experiments demonstrate a significant speedup over the state of the art, allowing for optimal solutions over longer horizons for many benchmark problems.