Heuristic variable grid solution method for POMDPs

Research output: Contribution to conferencePaperpeer-review

36 Scopus citations

Abstract

Partially observable Markov decision processes (POMDPs) are an appealing tool for modeling planning problems under uncertainty. They incorporate stochastic action and sensor descriptions and easily capture goal oriented and process oriented tasks. Unfortunately, POMDPs are very difficult to solve. Exact methods cannot handle problems with much more than 10 states, so approximate methods must be used. In this paper, we describe a simple variable-grid solution method which yields good results on relatively large problems with modest computational effort.

Original languageEnglish
Pages727-733
Number of pages7
StatePublished - 1 Dec 1997
Externally publishedYes
EventProceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 - Providence, RI, USA
Duration: 27 Jul 199731 Jul 1997

Conference

ConferenceProceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97
CityProvidence, RI, USA
Period27/07/9731/07/97

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