Artificial Intelligence
Quantitative deduction and its fixpoint theory
Journal of Logic Programming
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Paraconsistent logic programming
Theoretical Computer Science
Non-monotonic negation in probabilistic deductive databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Bilattices and the semantics of logic programming
Journal of Logic Programming
Foundations of disjunctive logic programming
Foundations of disjunctive logic programming
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Empirical probabilities in Monadic deductive databases
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Probabilistic logic programming
Information and Computation
Stable semantics for probabilistic deductive databases
Information and Computation
ACM Transactions on Database Systems (TODS)
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
The Semantics of Predicate Logic as a Programming Language
Journal of the ACM (JACM)
ACM Transactions on Computational Logic (TOCL)
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Default Logic as a Query Language
IEEE Transactions on Knowledge and Data Engineering
On the Semantics of Rule-Based Expert Systems with Uncertainty
ICDT '88 Proceedings of the 2nd International Conference on Database Theory
Procedural Semantics for Fuzzy Disjunctive Programs
LPAR '02 Proceedings of the 9th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Annals of Mathematics and Artificial Intelligence
A top-k query answering procedure for fuzzy logic programming
Fuzzy Sets and Systems
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A new knowledge representation language, called QDLP, which extends DLP to deal with uncertain values is introduced. Each (quantitative) rule is assigned a certainty degree interval (a subinterval of [0,1]). The propagation of uncertainty information from the premises to the conclusion of a quantitative rule is achieved by means of triangular norms (T‐norms). Different T‐norms induce different semantics for one given quantitative program. In this sense, QDLP is parameterized and each choice of a T‐norm induces a different QDLP language. Each T‐norm is eligible for events with determinate relationships (e.g., independence, exclusiveness) between them. Since there are infinitely many T‐norms, it turns out that there is a family of infinitely many QDLP languages. This family is carefully studied and the set of QDLP languages which generalize traditional DLP is precisely singled out. Algorithms for computing the minimal models of quantitative programs are proposed.