Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs

Frans A. Oliehoek, Matthijs T. J. Spaan, Christopher Amato, and Shimon Whiteson. Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs. Journal of Artificial Intelligence Research, 46:449–509, 2013.


pdf [7.2MB]  


This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm, which reduces the problem to a tree of one-shot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of Dec-POMDPs that can be solved optimally. First, we introduce lossless incremental clustering of the CBGs solved by GMAA*, which achieves exponential speedups without sacrificing optimality. Second, we introduce incremental expansion of nodes in the GMAA* search tree, which avoids the need to expand all children, the number of which is in the worst case doubly exponential in the node's depth. This is particularly beneficial when little clustering is possible. In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger Dec-POMDPs. We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent. Finally, we present extensive empirical results demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can optimally solve Dec-POMDPs of unprecedented size.

BibTeX Entry

  author =       {Frans A. Oliehoek and Matthijs T. J. Spaan and
                  Christopher Amato and Shimon Whiteson},
  title =        {Incremental Clustering and Expansion for Faster
                  Optimal Planning in {Dec-POMDPs}},
  journal =      {Journal of Artificial Intelligence Research},
  year =         2013,
  volume =       46,
  pages =        {449--509}

Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Generated by (written by Patrick Riley) on Wed Apr 10, 2019 18:58:24 UTC