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Belief Logic Programming and its Extensions ∗

by Hui Wan, Michael Kifer , 2009
"... 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 ..."
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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 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. Declarative semantics is provided as well as query evaluation algorithms. Also BLP is extended to to programs with cycles and to correlated base facts. The results are illustrated via simple, yet realistic examples of rule-based Web service integration.
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Belief Logic Programming with Cyclic Dependencies

by Hui Wan
"... Abstract. 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 w ..."
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Abstract. 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. 1
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