The Utility of Knowledge in Inductive Learning

In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constant-free Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a rule and on the accuracy of a learned rule. Moreover, we demonstrate that a hybrid explanation-based and inductive learning method can advantageously use an approximate domain theory, even when this theory is incorrect and incomplete. 1 Introduction Most existing systems that combine empirical and explanation-based learning severely restrict the complexity of the language for expressing the concept definition. For example, some systems require that the concept definition be expressed in terms of attribute-value pairs (Lebowitz, 1986; Danyluk, 1989). Others effectively restrict the concept definition language to that of propositional calculus, by only allowing unary predicates (Hirsh, 1989;..