Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Inference methods for a pseudo-boolean satisfiability solver
Eighteenth national conference on Artificial intelligence
Nogood Recording for Valued Constraint Satisfaction Problems
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Pueblo: A Modern Pseudo-Boolean SAT Solver
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Graphically structured value-function compilation
Artificial Intelligence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Optimal recommendation sets: covering uncertainty over user preferences
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
DD-PREF: a language for expressing preferences over sets
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Multi-objective Russian Doll search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
Defining relative likelihood in partially-ordered preferential structures
Journal of Artificial Intelligence Research
Bidding languages for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning conditional preference networks
Artificial Intelligence
Preference-Based CBR: first steps toward a methodological framework
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Representing and reasoning with qualitative preferences for compositional systems
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
Various tasks in decision making and decision support systems require selecting a preferred subset of a given set of items. Here we focus on problems where the individual items are described using a set of characterizing attributes, and a generic preference specification is required, that is, a specification that can work with an arbitrary set of items. For example, preferences over the content of an online newspaper should have this form: At each viewing, the newspaper contains a subset of the set of articles currently available. Our preference specification over this subset should be provided offline, but we should be able to use it to select a subset of any currently available set of articles, e.g., based on their tags. We present a general approach for lifting formalisms for specifying preferences over objects with multiple attributes into ones that specify preferences over subsets of such objects. We also show how we can compute an optimal subset given such a specification in a relatively efficient manner. We provide an empirical evaluation of the approach as well as some worst-case complexity results.