COMBINING EXPLANATION-BASED LEARNING AND ARTIFICIAL NEURAL NETWORKS

Abstract

Explanation-based and neural learning algorithms each have their own strengths and weaknesses. An approach for combining these two styles of learning is presented. Rather than being arbitrarily chosen, initial neural network configurations are determined by the generalized explanation of the solutions to specific tasks. If these generalized explanations are not completely correct, the neural network refines them. Empirical tests with an initial implementation of the approach demonstrate that the hybrid out-performs each of the individual algorithms alone.