Planning and acting in partially observable stochastic domains
Artificial Intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning Communication in Interactive Dynamic Influence Diagrams
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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Multiagent POMDPs 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 broadcast information is delayed by at most one time step. Such an assumption is in fact more appropriate for applications in which response time is critical. However, naive application of incremental pruning, the core of many state-of-the-art POMDP techniques, is intractable for MPOMDP-DCs. We overcome this problem by introducing a tree-based pruning technique. Experiments show that the method outperforms naive incremental pruning by orders of magnitude, allowing for the solution of larger problems.