2013/03/20 by Raymond T. Ng, Ng, Raymond T., V. S. Subrahmanian +1
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Semantic Web and Ontologies #cs.AI
paper · pdf · doi:10.48550/arxiv.1303.5735
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)
arxiv created 2013/03/20 · openalex publication_date 2013/03/20 · arxiv updated 2013/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.