Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
BAGGER: an EBL system that extends and generalizes explanations
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
BAGGER: an EBL system that extends and generalizes explanations
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
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An approach to generalizing number in explanation-based learning is presented. Generalizing number can involve generalizing such things as the number of entities involved in a concept or the number of times some action is performed. This issue has been largely ignored in previous explanation-based learning research. Instead, other research has focused on changing constants into variables and determining the general constraints on those variables. In the approach presented, generalization to N is triggered by the detection of inference rules of a specified syntactic form. When one is found, it is extended into the rule that results from an arbitrary number of repeated applications of the original rule. If the preconditions of the extended rule are met, the results of multiple applications of the original rule are immediately determined. There is no need to apply the underlying rule successively, each time checking if the preconditions for the next application are satisfied.