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Tree-based Pruning for Multiagent POMDPs with Delayed Communication

Frans A. Oliehoek and Matthijs T. J. Spaan. Tree-based Pruning for Multiagent POMDPs with Delayed Communication. In Multi-agent Sequential Decision Making under Uncertainty, 2012. Workshop at AAMAS12

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Abstract

Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful framework for optimal decision making under the assumption of instantaneous communication. We focus on a delayed communication setting (MPOMDP-DC), in which broadcasted information is delayed by at most one time step. This model allows agents to act on their most recent (private) observation. Such an assumption is a strict generalization over having agents wait until the global information is available and is more appropriate for applications in which response time is critical. From a technical point of view, MPOMDP-DCs are quite similar to MPOMDPs. However, value function backups are significantly more costly, and naive application of incremental pruning, the core of many state-of-the-art optimal POMDP techniques, is intractable. In this paper, we show how to overcome this problem by demonstrating that computation of the MPOMDP-DC backup can be structured as a tree and introducing two novel tree-based pruning techniques that exploit this structure in an effective way. We experimentally show that these methods have the potential to outperform naive incremental pruning by orders of magnitude, allowing for the solution of larger problems.

BibTeX Entry

@InProceedings{Oliehoek12msdm,
  author =       {Frans A. Oliehoek and Matthijs T. J. Spaan},
  title =        {Tree-based Pruning for Multiagent {POMDPs} with
                  Delayed Communication},
  booktitle =    {Multi-agent Sequential Decision Making under Uncertainty},
  year =         2012,
  note =         {Workshop at AAMAS12}
}

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