AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Use of convex cones in interactive multiple objective decision making
Management Science
Handbook of discrete and computational geometry
A Logic of Relative Desire (Preliminary Report)
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Toward case-based preference elicitation: similarity measures on preference structures
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
Graphically structured value-function compilation
Artificial Intelligence
Preference elicitation in prioritized skyline queries
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity measures on preference structures, part ii: utility functions
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Overcoming incomplete user models in recommendation systems via an ontology
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
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Classical Decision Theory provides a normative framework for representing and reasoning about complex preferences. Straight forward application of this theory to automate decision making is difficult due to high elicitation cost. In response to this problem, researchers have recently developed a number of qualitative, logic-oriented approaches for representing and reasoning about preferences. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to preference elicitation and decision making that is grounded in classical multi-attribute utility theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify supoptimal alternatives. This work demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.