(Enter summary)
Abstract: In this paper, we describe the partially observable
Markov decision process (pomdp) approach to finding
optimal or near-optimal control strategies for partially
observable stochastic environments, given a complete
model of the environment. The pomdp approach was
originally developed in the operations research community
and provides a formal basis for planning problems
that have been of interest to the AI community.
We found the existing algorithms for computing optimal
control strategies to be... (Update)
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BibTeX entry: (Update)
Cassandra, A.; Kaelbling, L.; and Littman, M. 1994. Acting optimally in partially observable stochastic domains. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 1023--1028. http://citeseer.ist.psu.edu/cassandra94acting.html More
@inproceedings{ cassandra94acting,
author = "Anthony R. Cassandra and Leslie Pack Kaelbling and Michael L. Littman",
title = "Acting Optimally in Partially Observable Stochastic Domains",
booktitle = "Proceedings of the Twelfth National Conference on Artificial Intelligence ({AAAI}-94)",
volume = "2",
publisher = "AAAI Press/MIT Press",
address = "Seattle, Washington, USA",
isbn = "0-262-51078-2",
pages = "1023--1028",
year = "1994",
url = "citeseer.ist.psu.edu/cassandra94acting.html" }
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268
Dynamic Programming and Markov Processes (context) - Howard - 1960
216
The optimal control of partially observable markov processes.. (context) - Smallwood, Sondik - 1973
216
The Optimal Control of Partially Observable Markov Processes (context) - Sondik - 1971
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A formal theory of knowledge and action (context) - Moore - 1985
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Algorithms for Partially Observable Markov Decision Processe.. (context) - Cheng - 1988
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Algorithms for partially observable markov decision processe.. (context) - Cassandra, Kaelbling et al. - 1994
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Overcoming incomplete perception with utile distinction memo.. (context) - Operations, -- - 1993
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