Probabilistic Disjunctive Logic Programming
This work addresses a specific challenge in AI for combining logical and probabilistic reasoning, but it appears incremental as it builds on existing methods like Poole's Probabilistic Horn Abduction.
The paper tackles the problem of integrating probabilistic reasoning with disjunctive logic programming by proposing a framework that uses hypotheses and minimal models to derive default probability distributions when exact probabilities are unavailable, and presents an algorithm for computing default probabilities of goals.
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.