On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Integration of weighted knowledge bases
Artificial Intelligence
DISTANCE-SAT: complexity and algorithms
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Parameters for Utilitarian Desires in a Qualitative Decision Theory
Applied Intelligence
Two Proof Procedures for a Cardinality Based Language in Propositional Calculus
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Merging with Integrity Constraints
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Introducing actions into qualitative simulation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Reasoning with Uncertainty by Nmatrix---Metric Semantics
WoLLIC '08 Proceedings of the 15th international workshop on Logic, Language, Information and Computation
A framework for reasoning under uncertainty based on non-deterministic distance semantics
International Journal of Approximate Reasoning
Semantic distance measure between ontology concept's attributes
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
A defeasible reasoning model of inductive concept learning from examples and communication
Artificial Intelligence
Distance-Based measures of inconsistency
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Distances between possible worlds play an important role in logic-based knowledge representation (especially in belief change, reasoning about action, belief merging and similarity-based reasoning). We show here how they can be used for representing in a compact and intuitive way the preference profile of an agent, following the principle that given a goal G, then the closer a world w to a model of G, the better w. We give an integrated logical framework for preference representation which handles weighted goals and distances to goals in a uniform way. Then we argue that the widely used Hamming distance (which merely counts the number of propositional symbols assigned a different value by two worlds) is generally too rudimentary and too syntax-sensitive to be suitable in real applications; therefore, we propose a new family of distances, based on Choquet integrals, in which the Hamming distance has exactly a position very similar to that of the arithmetic mean in the class of Choquet integrals for multi-criteria decision making.