Machine learning and knowledge acquisition represent two complementary approaches to the acquisition and organization of knowledge for knowledge-based systems. Machine learning has focused on developing autonomous algorithms for acquiring knowledge as data and for knowledge compilation and organizat...

Full description

Bibliographic Details

Format:	Book
Language:	English
Published:	London [u.a.] Academic Press 1995
Series:	Knowledge-based systems 14
Subjects:	Acquisition des connaissances (systèmes experts) Apprentissage automatique Kennisrepresentatie Kennisverwerving Machine-learning Künstliche Intelligenz Artificial intelligence Expert systems (Computer science) Knowledge acquisition (Expert systems) Machine learning Wissenserwerb Maschinelles Lernen Lernender Automat Aufsatzsammlung
Summary:	Machine learning and knowledge acquisition represent two complementary approaches to the acquisition and organization of knowledge for knowledge-based systems. Machine learning has focused on developing autonomous algorithms for acquiring knowledge as data and for knowledge compilation and organization. In contrast, knowledge acquisition has focused on improving and partially automating the acquisition of knowledge from human experts by knowledge engineers Currently, both fields are moving towards an integrated approach using machine learning techniques to automate knowledge acquisition from experts, and knowledge acquisition techniques to guide and assist the learning process This is the first book to present some of the most representative approaches to the integration of machine learning and knowledge acquisition such as case-based reasoning, apprenticeship learning, knowledge base refinement through multistrategy learning, example-guided knowledge based revision, and interactive inductive logic programming. It also presents their application to such areas as planning, scheduling, diagnosis, control, information retrieval and robotics
Physical Description:	VI, 325 S. graph. Darst.
ISBN:	0126851204