Evidential support logic programming
Fuzzy Sets and Systems
On the semantics of rule-based expert systems with uncertainty
Lecture notes in computer science on ICDT '88
Logic programming with strong negation
Proceedings of the international workshop on Extensions of logic programming
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Using Dempster-Shafer's Theory of Evidence to Combine Aspects of Information Use
Journal of Intelligent Information Systems
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
The Paradoxical Success of Fuzzy Logic
IEEE Expert: Intelligent Systems and Their Applications
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Bayesian Logic Programs
Reasoning about Uncertainty
A new combination of evidence based on compromise
Fuzzy Sets and Systems
Logic Programming with Defaults and Argumentation Theories
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Probabilistic inductive logic programming
Probabilistic inductive logic programming
The independent choice logic and beyond
Probabilistic inductive logic programming
Query Answering in Belief Logic Programming
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Belief Logic Programming with Cyclic Dependencies
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
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Belief Logic Programming (BLP) is a novel form of quantitative logic programming in the presence of uncertain and inconsistent information, which was designed to be able to combine and correlate evidence obtained from non-independent information sources. BLP has non-monotonic semantics based on the concepts of belief combination functions and is inspired by Dempster-Shafer theory of evidence. Most importantly, unlike the previous efforts to integrate uncertainty and logic programming, BLP can correlate structural information contained in rules and provides more accurate certainty estimates. The results are illustrated via simple, yet realistic examples of rule-based Web service integration.