Communications of the ACM
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Changing the rules: a comprehensive approach to theory refinement
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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In this paper, we address an issue that arises when the background knowledge used by explanationbased learning is incorrect. In particular, we consider the problems that can be caused by a domain theory that may be overly specific. Under this condition, generalizations formed by explanation-based learning will make errors of omission when they are relied upon to make predictions or explanations. We describe a technique for detecting errors of omission, assigning blame for the error of omission to an inference rule in the domain theory, and revising the domain theory to accommodate new examples.