Artificial Intelligence
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
Paraconsistent logic programming
Proc. of the seventh conference on Foundations of software technology and theoretical computer science
Default reasoning and possibility theory
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
RI: A logic for reasoning with inconsistency
Proceedings of the Fourth Annual Symposium on Logic in computer science
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
A semantical framework for supporting subjective and conditional probabilities in deductive databases
Bilattices and the semantics of logic programming
Journal of Logic Programming
Probabilistic reasoning in logic programming
Methodologies for intelligent systems, 5
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Stable and extension class theory for logic programs and default logics
Journal of Automated Reasoning
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Uncertainty, belief, and probability
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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In this paper we study the uses and the semantics of non-monotonic negation in probabilistic deductive data bases. Based on the stable semantics for classical logic programming, we introduce the notion of stable formula, functions. We show that stable formula, functions are minimal fixpoints of operators associated with probabilistic deductive databases with negation. Furthermore, since a. probabilistic deductive database may not necessarily have a stable formula function, we provide a stable class semantics for such databases. Finally, we demonstrate that the proposed semantics can handle default reasoning naturally in the context of probabilistic deduction.