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Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion

Matthijs T. J. Spaan, Frans A. Oliehoek, and Christopher Amato. Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion. In Proc. of International Joint Conference on Artificial Intelligence, pp. 2027–2032, 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.

BibTeX Entry

@InProceedings{Spaan11ijcai,
  author =       {Matthijs T. J. Spaan and Frans A. Oliehoek and
                  Christopher Amato},
  title =        {Scaling Up Optimal Heuristic Search in {Dec-POMDPs}
                  via Incremental Expansion},
  booktitle =    {Proc. of International Joint Conference on
                  Artificial Intelligence},
  year =         2011,
  pages =        {2027--2032}
}

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