Uncertainty in Artificial Intelligence
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Non-monotonic Negation in Probabilistic Deductive Databases
Raymond Ng, V. Subrahmanian
Abstract:
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.
Keywords:
Pages: 249-256
PS Link:
PDF Link: /papers/91/p249-ng.pdf
BibTex:
@INPROCEEDINGS{Ng91,
AUTHOR = "Raymond Ng and V. Subrahmanian",
TITLE = "Non-monotonic Negation in Probabilistic Deductive Databases",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "249--256"
}


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