2013/02/13 by Liem Viet Ngo, Ngo, Liem
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Logic, programming, and type systems #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1302.3592
openalex publication_date 2013/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we propose a framework for combining Disjunctive Logic Programming and Poole's Probabilistic Horn Abduction. We use the concept of hypothesis to specify the probability structure. We consider the case in which probabilistic information is not available. Instead of using probability intervals, we allow for the specification of the probabilities of disjunctions. Because minimal models are used as characteristic models in disjunctive logic programming, we apply the principle of indifference on the set of minimal models to derive default probability values. We define the concepts of explanation and partial explanation of a formula, and use them to determine the default probability distribution(s) induced by a program. An algorithm for calculating the default probability of a goal is presented.