Bounding the cost of learned rules: A transformational approach

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Abstract

Cost reduction of explanation-based learning (EBL) systems is studied using transformational analysis. In this method, the learning process is decomposed into a sequence of transformations from the problem solving to the learned rules. The structure of the problem solving is varied from the match process from learned rules; and search control rules and the optimization employed in the problem solving are ignored. Results on a set of known expensive-rule learning tasks show that such modifications can effectively eliminate the identified set of sources of expensiveness.

Original languageEnglish
Pages1364
Number of pages1
StatePublished - 1996
EventProceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) - Portland, OR, USA
Duration: 4 Aug 19968 Aug 1996

Conference

ConferenceProceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2)
CityPortland, OR, USA
Period4/08/968/08/96

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