Quantitative deduction and its fixpoint theory
Journal of Logic Programming
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
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Probabilistic logic programming
Information and Computation
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
Constructing Flexible Dynamic Belief Networks from First-Order Probalistic Knowledge Bases
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Probabilistic Logic Programming under Inheritance with Overriding
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Bayesian Logic Programs
Machine Learning
Reasoning with recursive loops under the PLP framework
ACM Transactions on Computational Logic (TOCL)
Managing uncertainty and vagueness in description logics for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Logic Programming with Defaults and Argumentation Theories
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Belief Logic Programming: Uncertainty Reasoning with Correlation of Evidence
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Query Answering in Belief Logic Programming
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Probabilistic inductive logic programming
Probabilistic inductive logic programming
The independent choice logic and beyond
Probabilistic inductive logic programming
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Our previous work [26] introduced Belief Logic Programming (BLP), a novel form of quantitative logic programming with correlation of evidence. Unlike other quantitative approaches to logic programming, this new theory is able to provide accurate conclusions in the presence of uncertainty when the sources of information are not independent. However, the semantics defined in [26] is not sufficiently general--it does not allow cyclic dependencies among beliefs, which is a serious limitation of expressive power. This paper extends the semantics of BLP to allow cyclic dependencies. We show that the new semantics is backward compatible with the semantics for acyclic BLP and has the expected properties. The results are illustrated with examples of inference in a simple diagnostic expert system.