Uncertainty in Artificial Intelligence
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Similarity Measures on Preference Structures, Part II: Utility Functions
Vu Ha, Peter Haddawy, John Miyamoto
Abstract:
In previous work cite{Ha98:Towards} we presented a case-based approach to eliciting and reasoning with preferences. A key issue in this approach is the definition of similarity between user preferences. We introduced the probabilistic distance as a measure of similarity on user preferences, and provided an algorithm to compute the distance between two partially specified {em value} functions. This is for the case of decision making under {em certainty}. In this paper we address the more challenging issue of computing the probabilistic distance in the case of decision making under{em uncertainty}. We provide an algorithm to compute the probabilistic distance between two partially specified {em utility} functions. We demonstrate the use of this algorithm with a medical data set of partially specified patient preferences,where none of the other existing distancemeasures appear definable. Using this data set, we also demonstrate that the case-based approach to preference elicitation isapplicable in domains with uncertainty. Finally, we provide a comprehensive analytical comparison of the probabilistic distance with some existing distance measures on preferences.
Keywords:
Pages: 186-193
PS Link:
PDF Link: /papers/01/p186-ha.pdf
BibTex:
@INPROCEEDINGS{Ha01,
AUTHOR = "Vu Ha and Peter Haddawy and John Miyamoto",
TITLE = "Similarity Measures on Preference Structures, Part II: Utility Functions",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "186--193"
}


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