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

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

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...

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Bibliographic Details
Main Authors: Oliehoek, Frans A., Spaan, Matthijs T. J., Amato, Christopher, Whiteson, Shimon
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Association for the Advancement of Artificial Intelligence 2013
Online Access:http://hdl.handle.net/1721.1/80753
https://orcid.org/0000-0002-6786-7384

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http://hdl.handle.net/1721.1/80753
https://orcid.org/0000-0002-6786-7384

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