Region-based approximations for planning in stochastic domains

Authors:
Nevin L. Zhang;Wenju Liu
Affiliations:
Department of Computer Science, Hong Kong University of Science and Technology;Department of Computer Science, Hong Kong University of Science and Technology
Venue:
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Year:
1997

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Abstract

This paper is concerned with planning in stochastic domains by means of partially observable Markov decision processes (POMDPs). POMDPs are difficult to solve. This paper identifies a subclass of POMDPs called region observable POMDPs, which are easier to solve and can be used to approximate general POMDPs to arbitrary accuracy.