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A context model for knowledge-intensive case-based reasoning
 

Summary: 1
A context model for
knowledge-intensive case-based reasoning
Pinar Öztürk and Agnar Aamodt
Department of Computer and Information Science
Norwegian University of Science and Technology
N-7034 Trondheim, Norway
{pinar, agnar}@idi.ntnu.no
Abstract: Decision-support systems that help solving problems in open and weak theory domains, i.e. hard
problems, need improved methods to ground their models in real world situations. Models that attempt to
capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too
abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorpora-
tion of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a
changing environment by continuously retaining new problem solving cases as they occur during normal
system operation. The research reported in this paper describes an extension that incorporates additional
knowledge of the problem solving context into the architecture. The components of this context model is
described, and related to the roles the components play in an abductive diagnostic process. Background
studies are summarized, the context model is explained, and an example shows its integration into an exist-
ing knowledge-intensive CBR system.
1 Introduction

  

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

 

Collections: Computer Technologies and Information Sciences