Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Logical representation and computation of optimal decisions in a qualitative setting
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference
IEEE Transactions on Knowledge and Data Engineering
Preferences; Putting More Knowledge into Queries
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Reasoning in Inconsistent Stratified Knowledge Bases
ISMVL '96 Proceedings of the 26th International Symposium on Multiple-Valued Logic
Qualitative relevance and independence: a roadmap
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
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The classical way of encoding preferences in decision theory is by means of utility or value functions. However agents are not always able to deliver such a function directly. In this paper, we relate three different ways of specifying preferences, namely by means of a set of particular types of constraints on the utility function, by means of an ordered set of prioritized goals expressed by logical propositions, and by means of an ordered set of subsets of possible candidates reaching the same level of satisfaction. These different expression modes can be handled in a weighted logical setting, here the one of possibilistic logic. The aggregation of preferences pertaining to different criteria can then be handled by fusing sets of prioritized goals. Apart from a better expressivity, the benefits of a logical representation of preferences are to put them in a suitable format for reasoning purposes, or for modifying or revising them.