Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Handbook of logic in artificial intelligence and logic programming (vol. 3)
An overview of ordinal and numerical approaches to causal diagnostic problem solving
Handbook of defeasible reasoning and uncertainty management systems
Parameters for Utilitarian Desires in a Qualitative Decision Theory
Applied Intelligence
Knowledge-Driven versus Data-Driven Logics
Journal of Logic, Language and Information
Anytime Possibilistic Propagation Algorithm
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Bridging Logical, Comparative, and Graphical Possibilistic Representation Frameworks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Bipolarity in Possibilistic Logic and Fuzzy Rules
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Preference-based argumentation: Arguments supporting multiple values
International Journal of Approximate Reasoning
Handling bipolarity in elementary queries to possibilistic databases
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Graded BDI models for agent architectures
CLIMA'04 Proceedings of the 5th international conference on Computational Logic in Multi-Agent Systems
Bipolar queries: An aggregation operator focused perspective
Fuzzy Sets and Systems
A bipolar possibilistic representation of knowledge and preferences and its applications
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
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Recently, it has been emphasized that the possibility theory framework allows us to distinguish between i) what is possible because it is not ruled out by the available knowledge, and ii) what is possible for sure. This distinction may be useful when representing knowledge, for modelling values which are not impossible because they are consistent with the available knowledge on the one hand, and values guaranteed to be possible because reported from observations on the other hand. It is also of interest when expressing preferences, to point out values which are positively desired among those which are not rejected. This distinction can be encoded by two types of constraints expressed in terms of necessity measures and in terms of guaranteed possibility functions, which induce a pair of possibility distributions at the semantic level. A consistency condition should ensure that what is claimed to be guaranteed as possible is indeed not impossible. The present paper investigates the representation of this bipolar view, including the case when it is stated by means of conditional measures, or by means of comparative context-dependent constraints. The interest of this bipolar framework, which has been recently stressed for expressing preferences, is also pointed out in the representation of diagnostic knowledge.