Probabilistic reasoning in intelligent systems: networks of plausible inference
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Problem-focused incremental elicitation of multi-attribute tility models
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A POMDP formulation of preference elicitation problems
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Visual exploration and incremental utility elicitation
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Constraint-based optimization and utility elicitation using the minimax decision criterion
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Learning a decision maker's utility function from (possibly) inconsistent behavior
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On the foundations of expected expected utility
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Journal of Artificial Intelligence Research
Learning a decision maker's utility function from (possibly) inconsistent behavior
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Constraint-based optimization and utility elicitation using the minimax decision criterion
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Modeling challenges with influence diagrams: Constructing probability and utility models
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Reasoning with conditional ceteris paribus preference statements
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A hybrid approach to reasoning with partially elicited preference models
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Toward case-based preference elicitation: similarity measures on preference structures
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Applied Bionics and Biomechanics
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We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two common approaches to utility function elicitation. The first is to base the determination of the user's utility function solely on elicitation of qualitative preferences. The second makes assumptions about the form and decomposability of the utility function. Here we take a different approach: we attempt to identify the new user's utility function based on classification relative to a database of previously collected utility functions. We do this by identifying clusters of utility functions that minimize an appropriate distance measure. Having identified the clusters, we develop a classification scheme that requires many fewer and simpler assessments than full utility elicitation and is more robust than utility elicitation based solely on preferences. We have tested our algorithm on a small database of utility functions in a prenatal diagnosis domain and the results are quite promising.