Similarity Measures on Preference Structures, Part II: Utility Functions
arXiv:1301.2274
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.
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)