Uncertainty in Artificial Intelligence
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Region-Based Approximations for Planning in Stochastic Domains
Nevin Zhang, Wenju Liu
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.
Keywords: Planning under uncertainty, partially observable Markov decision processes, problem c
Pages: 472-480
PS Link: file://ftp.cs.ust.hk/pub/lzhang/uai97liu.ps.gz
PDF Link: /papers/97/p472-zhang.pdf
AUTHOR = "Nevin Zhang and Wenju Liu",
TITLE = "Region-Based Approximations for Planning in Stochastic Domains",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "472--480"

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