Learning causal relationships: an integration of empirical and explanation-based learning methods
Learning causal relationships: an integration of empirical and explanation-based learning methods
Models of incremental concept formation
Artificial Intelligence
Search control, utility, and concept induction
Proceedings of the seventh international conference (1990) on Machine learning
Information filters and their implementation in the SYLLOG system
Proceedings of the sixth international workshop on Machine learning
A Critical Look at Experimental Evaluations of EBL
Machine Learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Empirical analysis of the general utility problem in machine learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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Recent research suggests the utility of performing induction over explanations. This process identifies commonalities across explanations that cannot be extracted solely by explanation-based techniques. This has important implications for the 'correctness' of learned knowledge [Flann and Dietterich, 1989] and, as we show, on the efficiency with which learned knowledge can be reused. Specifically, we illustrate that inductive concept formation can abstract and organize explanatory knowledge for efficient reuse in a domain of algebra story problems.