Abstract
Presenting multiple examples to an Explanation Based Learning system may lead to a lot of quite similar rules. This has a negative effect on the overall problem solving performance. The problem can be alleviated by combining several rules into one. We present a method to generalize rules by locating common parts and differences in order to obtain a more useful set of rules.
Cite
CITATION STYLE
Sablon, G., De Raedt, L., & Bruynooghe, M. (1989). Generalizing multiple examples in explanation based learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 397 LNAI, pp. 177–183). Springer Verlag. https://doi.org/10.1007/3-540-51734-0_60
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