To be considered intelligent, machines must be capable of learning. Symbolic and neural approaches to machine learning both have been heavily investigated. However, there has been little research into the synergies achievable by combining these two learning paradigms. This work will develop a hybrid system that combines the symbolically-oriented explanation-based learning paradigm with the neural back-propagation algorithm. In this system, the initial neural network configuration is determined by the generalized explanation of the solution to a specific classification of planning task. This research addresses the problem of choosing a good initial neural network configuration and overcomes problems that arise when using imperfect theories to build explanations. Most real-world problems can never be formalized exactly. However, there is much to be gained by utilizing the capability to reason approximately correctly. Explanation-based learning provides a way to profitably use casual models of the world, while neural networks provide a way to refine roughly-correct concepts. This research on combining these two learning paradigms promises to broaden the applicability of machine learning techniques, producing learning algorithms that are not brittle and which can produce concepts whose accuracy improves through experience.
