Uncertainty in Artificial Intelligence
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Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation
Blai Bonet, Judea Pearl
Abstract:
We develop a qualitative theory of Markov Decision Processes (MDPs) and Partially Observable MDPs that can be used to model sequential decision making tasks when only qualitative information is available. Our approach is based upon an order-of-magnitude approximation of both probabilities and utilities, similar to epsilon-semantics. The result is a qualitative theory that has close ties with the standard maximum-expected-utility theory and is amenable to general planning techniques.
Keywords:
Pages: 61-68
PS Link:
PDF Link: /papers/02/p61-bonet.pdf
BibTex:
@INPROCEEDINGS{Bonet02,
AUTHOR = "Blai Bonet and Judea Pearl",
TITLE = "Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "61--68"
}


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