Probabilistic Disjunctive Logic Programming

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 spec­ ify 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 mini­ mal models are used as characteristic mod­ els in disjunctive logic programming, we ap­ ply the principle of indifference on the set of minimal models to derive default probability values. We define the concepts of explana­ tion and partial explanation of a formula, and use them to determine the default probabil­ ity distribution(s) induced by a program. An algorithm for calculating the default proba­ bility of a goal is presented.