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G. Tecuci, Y. Kodratoff (eds.): Machine Learning and Knowledge Acquisition; Integrated Approaches, Academic Press, 1995, Ch. 8, 99 197-245
 

Summary: G. Tecuci, Y. Kodratoff (eds.): Machine Learning and Knowledge Acquisition; Integrated Approaches,
Academic Press, 1995, Ch. 8, 99 197-245
Knowledge Acquisition and Learning by Experience -
The Role of Case-Specific Knowledge
Agnar Aamodt
University of Trondheim, Department of Informatics, N-7055 Dragvoll, Norway
(Email: agnar@ifi.unit.no)
Abstract
As knowledge-based systems are addressing increasingly complex domains, their roles are
shifting from classical expert systems to interactive assistants. To develop and maintain
such systems, an integration of thorough knowledge acquisition procedures and sustained
learning from experience is called for. A knowledge level modeling perspective has
shown to be useful for analyzing the various types of knowledge related to a particular
domain and set of tasks, and for constructing the models of knowledge contents needed in
an intelligent system. To be able to meet the requirements of future systems with respect
to robust competence and adaptive learning behavior, particularly in open and weak
theory domains, a stronger emphasis should be put on the combined utilization of case-
specific and general domain knowledge. In this chapter we present a framework for
integrating KA and ML methods within a total knowledge modeling cycle, favoring an
iterative rather than a top down approach to system development. Recent advances in the

  

Source: Aamodt, Agnar - Department of Computer and Information Science, Norwegian University of Science and Technology

 

Collections: Computer Technologies and Information Sciences