A neural network approach to interference mechanism for logic programming language

Abstract

SUMMARYIt is known that the Hopfield-type neural network can solve the combinatorial optimization problem with a high speed. In order to realize that advantage, however, the energy function must be constructed for each problem, and there is no general framework to be applied to various problems. This paper notes that the logic programming language Prolog can be represented as the logical operation of facts and the rules, and proposes an algorithm, which converts the logical operation network to the Hopfield-type neural network. Using this conversion algorithm, the power of the Hopfield-type network as an optimization machine can be utilized in the logic inference described by Prolog. Then, it is expected that the range of its application will be enlarged greatly.