UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Logical Preference Representation and Combinatorial Vote
Annals of Mathematics and Artificial Intelligence
Graphically structured value-function compilation
Artificial Intelligence
An Efficient Upper Approximation for Conditional Preference
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Extending CP-nets with stronger conditional preference statements
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The computational complexity of dominance and consistency in CP-nets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Introducing variable importance tradeoffs into CP-nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Graphical representation of ordinal preferences: languages and applications
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Preferences in AI: An overview
Artificial Intelligence
Computational techniques for a simple theory of conditional preferences
Artificial Intelligence
Pruning rules for constrained optimisation for conditional preferences
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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A fundamental task for reasoning with preferences is the following: given input preference information from a user, and outcomes α and β, should we infer that the user will prefer α to β? For CP-nets and related comparative preference formalisms, inferring a preference of α over β using the standard definition of derived preference appears to be extremely hard, and has been proved to be PSPACE-complete in general for CP-nets. Such inference is also rather conservative, only making the assumption of transitivity. This paper defines a less conservative approach to inference which can be applied for very general forms of input. It is shown to be efficient for expressive comparative preference languages, allowing comparisons between arbitrary partial tuples (including complete assignments), and with the preferences being ceteris paribus or not.