Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Bipolar preference problems: framework, properties and solving techniques
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
A constraint satisfaction framework for decision under uncertainty
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the qualitative comparison of sets of positive and negative affects
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Possibility theory for reasoning about uncertain soft constraints
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Which Soft Constraints do you Prefer?
Electronic Notes in Theoretical Computer Science (ENTCS)
Preferences in AI: An overview
Artificial Intelligence
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Preferences and uncertainty are common in many real-life problems. In this paper, we focus on bipolar preferences and on uncertainty modelled via uncontrollable variables. However, some information is provided for such variables, in the form of possibility distributions over their domains. To tackle such problems, we eliminate the uncertain part of the problem, making sure that some desirable properties hold about the robustness of the problem's solutions and its relationship with their preference. We also define semantics to order the solutions according to different attitudes with respect to the notions of preference and robustness.