Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • Tree-Based Solution Methods for Multiagent POMDPs with Delayed CommunicationFrans A. Oliehoek and Matthijs T. J. Spaan. Tree-Based Solution Methods for Multiagent POMDPs with Delayed Communication. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI), pp. 1415–1421, July 2012. DownloadAbstractMultiagent 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. In this setting, 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 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{Oliehoek12AAAI_TBP, author = {Frans A. Oliehoek and Matthijs T. J. Spaan}, title = {Tree-Based Solution Methods for Multiagent {POMDPs} with Delayed Communication}, booktitle = AAAI12, month = jul, year = 2012, pages = {1415--1421}, url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5066}, abstract = { 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. In this setting, 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 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. } }
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