Learning Domain Theories using Abstract Background Knowledge

Reference: P. Clark and S. Matwin. Learning domain theories using abstract background knowledge. In P. Brazdil, editor, Proc. Sixth European Conference on Machine Learning (ECML-93), pages 360-365, 1993. Springer-Verlag.

Abstract: Substantial machine learning research has addressed the task of learning new knowledge given a (possibly incomplete or incorrect) domain theory, but leaves open the question of where such domain theories originate. In this paper we address the problem of constructing a domain theory from more general, abstract knowledge which may be available. The basis of our method is to first assume a structure for the target domain theory, and second to view background knowledge as constraints on components of that structure. This enables a focusing of search during learning, and also produces a domain theory which is explainable with respect to the background knowledge. We evaluate an instance of this methodology applied to the domain of economics, where background knowledge is represented as a qualitative model.