Uncertainty in Artificial Intelligence
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Towards Case-Based Preference Elicitation: Similarity Measures on Preference Structures
Vu Ha, Peter Haddawy
Abstract:
While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decision-theoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment directly elicited preferences with some appropriate default information. In this paper we propose a case-based approach to alleviating the preference elicitation bottleneck. Assuming the existence of a population of users from whom we have elicited complete or incomplete preference structures, we propose eliciting the preferences of a new user interactively and incrementally, using the closest existing preference structures as potential defaults. Since a notion of closeness demands a measure of distance among preference structures, this paper takes the first step of studying various distance measures over fully and partially specified preference structures. We explore the use of Euclidean distance, Spearmans footrule, and define a new measure, the probabilistic distance. We provide computational techniques for all three measures.
Keywords: Utility elicitation, case-based reasoning, similarity measures.
Pages: 193-201
PS Link: http://cs.uwm.edu/~vu/research.htm
PDF Link: /papers/98/p193-ha.pdf
BibTex:
@INPROCEEDINGS{Ha98,
AUTHOR = "Vu Ha and Peter Haddawy",
TITLE = "Towards Case-Based Preference Elicitation: Similarity Measures on Preference Structures",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "193--201"
}


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