Uncertainty in Artificial Intelligence
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Region-Based Approximations for Planning in Stochastic Domains
Nevin Zhang, Wenju Liu
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.
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
BibTex:
@INPROCEEDINGS{Zhang97,
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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