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Knowledge refinement using Knowledge Acquisition and Machine Learning Methods

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 599)

Abstract

APT system integrates Machine Learning (ML) and Knowledge Acquisition (KA) methods in the same framework. Both kinds of methods closely cooperate to concur in the same purpose: the acquisition, validation and maintenance of problem-solving knowledge. The methods are based on the same assumption: knowledge acquisition and learning are done through experimentation, classification and comparison of concrete cases. This paper details APT's mechanisms and shows through examples and applications how APT underlying principles allow various methods to fruitfully collaborate.

Keywords

Knowledge Acquisition Domain Theory Task Structure Task Decomposition Candidate Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  1. 1.CNRS & Laboratoire de Recherche en Informatique Equipe Inférence et ApprentissageUniversité Paris-SudOrsay CédexFrance
  2. 2.Chemin du MoulonISoftGif sur YvetteFrance

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