Most machine learning is focused on inductive generalization from empirical data and does not explicitly exploit prior knowledge of the domain. Explanation-based learning is a radically different approach that uses existing declarative domain knowledge to "explain" individual examples and uses this explanation to drive a knowledge-based generalization of the example. It is therefore capable of learning a very general concept from only a single training example. Our work was some of the original research on this approach and lead to our subsequent work on theory refinement and on learning for planning and problem-solving.