EXPLANATION-BASED LEARNING: ITS ROLE IN PROBLEM SOLVING
Date
1988-06-01
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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.
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Computer Science