(Enter summary)
Abstract: A general and expressive model of sequential decision
making under uncertainty is provided by
the Markov decision processes (MDPs) framework. (Update)
Context of citations to this paper: More
...polynomial in the size of the problem specification. As a result, their results parallel the results for finite horizon POMDPs. Rintanen [29] analyzes the complexity of probabilistic planning problems under average reward criteria, and establishes undecidability results for...
...size polynomial in the size of the problem speci cation. As a result, their results parallel the results for nite horizon POMDPs. Rintanen [45] analyzes the complexity of probabilistic planning problems under average reward criteria, and establishes undecidability results for...
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1.8: Complexity of Probabilistic Planning under Average Rewards - Rintanen (2001)
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0.5: Conditional Planning in the Discrete Belief Space - Jussi Rintanen.. (2005)
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0.4: Partial implicit unfolding in the Davis-Putnam procedure for.. - Rintanen (2001)
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0.3: Complexity of Planning with Partial Observability - Rintanen (2004)
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0.3: On the Undecidability of Probabilistic Planning and.. - Madani, Hanks, al. (1999)
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0.2: Constrained Discounted Markov Decision Processes And Hamiltonian .. - Feinberg (1997)
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BibTeX entry: (Update)
J. Rintanen. Complexity of probabilistic planning under average rewards. In Proceedings of International Joint Conference on Artificial Intelligence, 2001. To Appear. http://citeseer.ist.psu.edu/rintanen01complexity.html More
@inproceedings{ rintanen01complexity,
author = "Jussi Rintanen",
title = "Complexity of Probabilistic Planning under Average Rewards",
booktitle = "{IJCAI}",
pages = "503-508",
year = "2001",
url = "citeseer.ist.psu.edu/rintanen01complexity.html" }
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