Probabilistic Logic Programming under Inheritance with Overriding
This work addresses a specific issue in probabilistic logic programming for AI researchers, but appears incremental as it builds on existing approaches to default reasoning.
The paper tackles the problem of reasoning with conditional constraints in probabilistic logic programming by introducing inheritance with overriding, and presents new entailment relations, algorithms, and program transformations to improve efficiency.
We present probabilistic logic programming under inheritance with overriding. This approach is based on new notions of entailment for reasoning with conditional constraints, which are obtained from the classical notion of logical entailment by adding the principle of inheritance with overriding. This is done by using recent approaches to probabilistic default reasoning with conditional constraints. We analyze the semantic properties of the new entailment relations. We also present algorithms for probabilistic logic programming under inheritance with overriding, and program transformations for an increased efficiency.