Learning domain theories using abstract background knowledge

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.