Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
A logic of graded possibility and certainty coping with partial inconsistency
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
KRIS: Knowledge Representation and Inference System
ACM SIGART Bulletin - Special issue on implemented knowledge representation and reasoning systems
Inside the LOOM description classifier
ACM SIGART Bulletin - Special issue on implemented knowledge representation and reasoning systems
ACM SIGART Bulletin - Special issue on implemented knowledge representation and reasoning systems
A Hybrid Approach for Modeling Uncertainty in Terminological Logics
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Decidable reasoning in terminological knowledge representation systems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Possibilistic logic, preferential models, non-monotonicity and related issues
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Generalizing term subsumption languages to fuzzy logic
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
PossDL — a possibilistic DL reasoner for uncertainty reasoning and inconsistency handling
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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Possibilistic logic, an extension of first-order logic, deals with uncertainty that can be estimated in terms of possibility and necessity measures. Syntactically, this means that a first-order formula is equipped with a possibility degree or a necessity degree that expresses to what extent the formula is possibly or necessarily true. Possibilistic resolution yields a calculus for possibilistic logic which respects the semantics developed for possibilistic logic. A drawback, which possibilistic resolution inherits from classical resolution, is that it may not terminate if applied to formulas belonging to decidable fragments of first-order logic. Therefore we propose an alternative proof method for possibilistic logic. The main feature of this method is that it completely stracts from a concrete calculus but uses as basic operation a test for classical entailment. We then instantiate possibilistic logic with a terminological logic, which is a decidable subclass of first-order logic but nevertheless much more expressive than propositional logic. This yields an extension of terminological logics towards the representation of uncertain knowledge which is satisfactory from a semantic as well as algorithmic point of view.