‘Explanation-based’ learning is a semantically-driven. knowledge-intensive paradigm for machine learning which contrasts sharply with syntactic or ‘similarity-based’ approaches. This paper redevelops the foundations of EBL from the perspective of problem-solving. Viewed in this light, the technique is revealed as a simple modification to an inference engine which gives it the ability to generalize the conditions under which the solution to a particular problem holds. We show how to embed generalization invisibly within the problem solver, so that it is accomplished as inference proceeds rather than as a separate step. The approach is also extended to the more complex domain of planning to illustrate that it is applicable to a variety of logic-based problem-solvers and is by no means restricted to only simple ones. We argue against the current trend to isolate learning from other activity and study it separately, preferred instead to integrate it into the very heart of problem solving.