Explanation-Based Learning (EBL) depends on the ability of a system to explain to itself, based on the domain theory, that a given training example is a member of the target concept. However, in many complex domains it is often intractable to do this. In this paper I introduce a learning technique called Lazy Explanation-Based Learning as a solution to the problem of intractable explanation process in EBL. This technique is based on the idea that when the domain theory is intractable, it is

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... ible to learn by generalizing incomplete explanations and incrementally refining the over-general knowledge thus learned when met with unexpected plan failures. I describe a program that incrementally learns planning knowledge in game domains through Lazy Explanation-Based Learning. I present both empirical and theoretical evidence for the viability of Lazy Explanation-Based Learning.