Generalizing Number and Learning from Multiple Examples in Explanation Based Learning

Abstract

Explanation-based learning (EBL) systems have established their applicability to a wide variety of tasks. However, in despite intensive research, several problems relating to explanation-based learning have remained by and large open. This paper describes an approach to the problems of generalizing number and learning efficiently from multiple examples. The basic insight upon which the technique is based is that EBL can be thought of as learning control knowledge for a theorem-prover. By providing a richer representation for such control knowledge, more general rules can be learned: in particular, by providing looping constructs, rules which generalize number can be expressed; and by providing conditional branches, rules learned from different training examples can be combined. The technique described has been fully implemented, is domain-independent, and has been applied to a number of examples from the domain of VLSI circuit design.