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Abstract:
One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is the multiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, called abductive explanationbased learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results
