EXPLANATION-BASED LEARNING: ITS ROLE IN PROBLEM SOLVING

Abstract

"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, which involves maintaining and operating on a global world state, to illustrate that it is by no means restricted to toy problem-solvers. We argue against the current trend to isolate learning from other activity and study it separately, preferring instead to integrate it into the very heart of problem solving.