Artificial Intelligence
Using approximate reasoning to represent default knowledge
Artificial Intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Resolution principles in possibilistic logic
International Journal of Approximate Reasoning
On the semantics of fuzzy logic
International Journal of Approximate Reasoning
Handling uncertain knowledge in an ATMS using possibilistic logic
Methodologies for intelligent systems, 5
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
Semantic Evaluation in Possibilistic Logic, Application to Min-Max Discrete Optimisation Problems
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Theorem proving under uncertainty: a possibility theory-based approach
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Possibilistic logic, preferential models, non-monotonicity and related issues
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A possibilistic approach to intrusion detection under imperfect logging protocol
Proceedings of the 6th International Conference on Security of Information and Networks
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A semantics is given to possibilistic logic, a logic that handles weighted classical logic formulae, and where weights are interpreted as lower bounds on degrees of certainty or possibility, in the sense of Zadeh's possibility theory. The proposed semantics is based on fuzzy sets of interpretations. It is tolerant to partial inconsistency. Satisfiability is extended from interpretations to fuzzy sets of interpretations, each fuzzy set representing a possibility distribution describing what is known about the state of the world. A possibilistic knowledge base is then viewed as a set of possibility distributions that satisfy it. The refutation method of automated deduction in possibilistic logic, based on previously introduced generalized resolution principle is proved to be sound and complete with respect to the proposed semantics, including the case of partial inconsistency.