An application of disjunctive logic programming with incomplete information

Despite their different perspectives, probabilistic reasoning and disjunctive logic programming strive for similar goals. Both address the problem of representing incomplete information and providing conclusions under reasonable assumptions such as independencies or closed world assumptions. This paper investigates the evaluation of independence assumptions which can be characterized by graphs (MARKOV and BAYESIAN networks). The underlying independence axioms are well-known-they correspond to a set of disjunctive clauses. Based on a compact representation of disjunctive clauses, called clause trees, we provide an efficient Δ-iteration technique with subsumption, which allows us to compute the least fixpoint of the program or alternatively the verification of particular independences. Experimental results revealed that our approach is much more efficient than the conventional evaluation techniques