Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem
type: Item
instance of: scholarly article
title: Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem
zbMATH Open document ID: 0754.68094
author: W. W. Cohen
published in: Machine Learning
publication date: 1992-09-27T00:00:00Z
review text: Frequently, as an effect of the incompleteness and incorrectness of the available information, domain theories produce multiple explanations for training instances, not all of them are correct. The issue proposes the abductive explanation-based learning (A-EBL) as an attempt to solve the multiple inconsistent explanation problem mainly based on using negative examples and set covering techniques. The EBL/TS and the abductive-EBL algorithms are presented in the second section. A formal treatment of A-EBL and EBL/TS, expressed in terms of theoretical results concerning competence and convergence properties, is developed in the next section. Some experimental results on A-EBL are also reported. It is proved that the A-EBL has convergence properties only logarithmically worse than EBL/TS and that A-EBL is computationally efficient when the domain theory is tractable.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 57634
zbMATH Keywords: theory specialization
zbMATH Keywords: theory revision
zbMATH Keywords: probably approximately correct learning
zbMATH Keywords: explanation-based learning
reviewed by: Luminita State
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 57634