Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Using arguments for making decisions: a possibilistic logic approach
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Qualitative decision under uncertainty: back to expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On the foundations of qualitative decision theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
On the axiomatization of qualitative decision criteria
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Using CP-nets as a guide for countermeasure selection
Proceedings of the 2007 ACM symposium on Applied computing
Journal of Artificial Intelligence Research
Uncertainty in bipolar preference problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
ICLP'05 Proceedings of the 21st international conference on Logic Programming
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Preferences and uncertainty occur in many real-life problems. The theory of possibility is one non-probabilistic way of dealing with uncertainty, which allows for easy integration with fuzzy preferences. In this paper we consider an existing technique to perform such an integration and, while following the same basic idea, we propose various alternative semantics which allow us to observe both the preference level and the robustness w.r.t. uncertainty of the complete instantiations. We then extend this technique to other classes of soft constraints, proving that certain desirable properties still hold.