Partially Observable Markov Decision Processes (POMDPs) are powerful models for planning under uncertainty in partially observable domains. However, computing optimal solutions for POMDPs is challenging because of the high computational requirements of POMDP solution algorithms. Several algorithms use a subroutine to prune dominated vectors in value functions, which requires a large number of linear programs (LPs) to be solved and it represents a large part of the total running time. In this paper we show how the LPs in POMDP pruning subroutines can be decomposed using a Benders decomposition. The resulting algorithm incrementally adds LP constraints and uses only a small fraction of the constraints. Our algorithm significantly improves the performance of existing pruning methods and the commonly used incremental pruning algorithm. Our new variant of incremental pruning is the fastest optimal pruning-based POMDP algorithm.
Original languageEnglish
Title of host publicationProceedings of the 31st Conference on Artificial Intelligence, AAAI 2017
Number of pages3678
StatePublished - 2017
EventAAAI'17 - San Francisco, United States


Abbreviated title AAAI Conference on Artificial Intelligence
CountryUnited States
CitySan Francisco
Internet address

ID: 32868683