A Connectionist Model for Weighted Fuzzy Programs

The usefulness of the results of logic programming in real-life applications is sometimes limited due to the inability of this theory to model the uncertain and dynamic character of real environments. Fuzzy logic programming has been lately considered as an important framework for handling uncertainty in logic programming systems. Still, there is a need for modelling adaptation of logic programs and the progress in this area is rather slow. In the present paper, we first extend fuzzy logic programs in a direction that brings them closer to the connectionist approach: we introduce weighted fuzzy programs, which allow the association of significance weights with the atoms that make up the body of a logic rule. The weights add expressiveness to the programs and allow the determination of the degree with which an antecedent affects the value of the rule consequent. Then, we propose a neural network implementation of weighted fuzzy programs that is capable of computing the minimal Herbrand model of a weighted fuzzy program.