ML Anthology

Region-Based Approximations for Planning in Stochastic Domains

Nevin Lianwen Zhang, Wenju Liu
UAI 1997 pp. 472-480
/uai/1997/zhang1997uai-region/

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.

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Cite

Text

Zhang and Liu. "Region-Based Approximations for Planning in Stochastic Domains." Conference on Uncertainty in Artificial Intelligence, 1997.

Markdown

[Zhang and Liu. "Region-Based Approximations for Planning in Stochastic Domains." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/zhang1997uai-region/)

BibTeX

@inproceedings{zhang1997uai-region,
  title     = {{Region-Based Approximations for Planning in Stochastic Domains}},
  author    = {Zhang, Nevin Lianwen and Liu, Wenju},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1997},
  pages     = {472-480},
  url       = {https://mlanthology.org/uai/1997/zhang1997uai-region/}
}