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PROBLEM SOLVING AND SUSTAINED LEARNING FROM EXPERIENCE: ANALYZING METHODS WITH RESPECT TO DOMAIN CHARACTERISTICS
 

Summary: PROBLEM SOLVING AND SUSTAINED LEARNING FROM EXPERIENCE: ANALYZING
METHODS WITH RESPECT TO DOMAIN CHARACTERISTICS
Agnar Aamodt
University of Trondheim
Dept. of Informatics
N-7055 Dragvoll, Norway
email: agnar@ifi.unit.no
Klaus-Dieter Althoff
University of Kaiserslautern
Dept. of Computer Science
D-67653 Kaiserslautern, Germany
email: althoff@informatik.uni-kl.de
Introduction
Integration of learning and problem solving may start out from different goals, and be viewed from different perspectives. One
example is "concept formation" as a goal, and the formation and utilization of operationalization criteria related to the problem
solving solving task, as the perspective. Another example is "improved performance" as a goal, and the improvement of total
problem solving speed - for computer and human together - as the perspective. A third example is "sustained learning", i.e.
continuous learning through problem solving experience, as a goal, and the impact of the application problem task on the
learning method as a perspective. Many more examples may be given, and for each of them a particular area of overlap, an
"intersection space" between machine learning (ML) and problem solving (PS) methods can be identified. Within this space,

  

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

 

Collections: Computer Technologies and Information Sciences