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Summary: Knowledge-Intensive Case-Based Reasoning in CREEK
Agnar Aamodt
Department of Computer and Information Science
Norwegian University of Science and Technology (NTNU)
NO-7491 Trondheim
Norway
agnar.aamodt@idi.ntnu.no
Abstract. Knowledge-intensive CBR assumes that cases are enriched with
general domain knowledge. In CREEK, there is a very strong coupling between
cases and general domain knowledge, in that cases are embedded within a
general domain model. This increases the knowledge-intensiveness of the cases
themselves. A knowledge-intensive CBR method calls for powerful knowledge
acquisition and modeling techniques, as well as machine learning methods that
take advantage of the general knowledge represented in the system. The
focusing theme of the paper is on cases as knowledge within a knowledge-
intensive CBR method. This is made concrete by relating it to the CREEK
architecture and system, both in general terms, and through a set of example
projects where various aspects of this theme have been studied.
1 Introduction
A knowledge-intensive case-based reasoning method assumes that cases, in some
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