AAAI'06 proceedings of the 21st 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
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Generic preferences over subsets of structured objects
Journal of Artificial Intelligence Research
Learning optimal subsets with implicit user preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Logic in databases: report on the LID 2008 workshop
ACM SIGMOD Record
Evaluation of set-based queries with aggregation constraints
Proceedings of the 20th ACM international conference on Information and knowledge management
Hi-index | 0.00 |
Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of feasible items. Recent work in this area considered methods for specifying such preferences based on the attribute values of individual elements within the set. Of these, the approach of (Brafman et al. 2006) appears to be the most general. In this paper, we consider the problem of computing an optimal subset given such a specification. The problem is shown to be NP-hard in the general case, necessitating heuristic search methods. We consider two algorithm classes for this problem: direct set construction, and implicit enumeration as solutions to appropriate CSPs. New algorithms are presented in each class and compared empirically against previous results.