Probabilistic Logic Programming under Inheritance with Overriding

TL;DR

This paper introduces a novel probabilistic logic programming framework that incorporates inheritance with overriding, enabling more nuanced reasoning with conditional constraints and improving computational efficiency.

Contribution

It develops new entailment notions for probabilistic default reasoning, analyzes their semantic properties, and provides algorithms and transformations for practical implementation.

Findings

New entailment relations for probabilistic inheritance reasoning.

Semantic analysis of the proposed entailment relations.

Algorithms and transformations for efficient probabilistic logic programming.

Abstract

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.