Semantical framework for supporting subjective and conditional probabilities in deductive databases

We present a theoretical basis for supporting subjective and conditional probabilities in deductive databases. We design a language that allows a user greater expressive power than classical logic programming. In particular, a user can express the fact that A is possible (i.e. A has non-zero probability), B is possible, but (A and B) as a whole is impossible. A user can also freely specify probability annotations that may contain variables. The focus of this paper is to study the semantics of programs written in such a language in relation to probability theory. Our model theory which is founded on the classical one captures the uncertainty described in a probabilistic program at the level of Herbrand Interpretations. We show that this model theory satisfies Fenstad's criteria for a function to be called probabilistic [5]. Furthermore, we develop a fixpoint semantics and a proof procedure for such programs. We characterize the relationships between probability theory and the fixpoint, model, and proof theory of our programs.