Learning Domain Theories using Abstract Beckground Knowledge

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 from in the rst place. In this paper we address the problem of constructing a domain theory itself from more general, abstract knowledge which may be available. The basis of our method is to rst assume a structure of 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 present a general framework for this task and describe learning algorithms which can be employed, and then apply a particular instance of it to the domain of economics. In this application domain, the background knowledge is a qualitative model expressing plausible economic relationships, examples are sets of numeric economic data, and the learning task is to induce a domain theory for predicting the future movement of economic parameters from this qualitative background knowledge and data. We evaluate the value of this approach, and nally speculate on ways this method could be extended.