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A Computational Model of Knowledge-Intensive Learning and Problem Solving1
 

Summary: 1
A Computational Model of Knowledge-Intensive
Learning and Problem Solving1
Agnar Aamodt
Knowledge Engineering Laboratory, ELAB-RUNIT, SINTEF
N-7034 Trondheim-NTH, Norway
and
Department of Electrical Engineering and Computer Science
University of Trondheim
agnar.aamodt@elab-runit.sintef.no
Abstract. If knowledge-based systems are to become more competent and robust in
solving real world problems, they need to be able to adapt to an evolving domain
and a changing environment. This paper proposes a computational model - a
framework -for knowledge-intensive problem solving and learning from experience.
The model has been instantiated in an architecture for knowledge-intensive case-
based reasoning and learning called CREEK (Case-based Reasoning through
Extensive Expert Knowledge). The importance of a thorough, extensive knowledge
model to support the reasoning and learning processes is emphasized. In case-based
reasoning a problem is solved by retrieving a similar past problem case, and using
this case in solving the new problem. Learning becomes a process of extracting

  

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

 

Collections: Computer Technologies and Information Sciences