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 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.
