Relating Case-Based Problem Solving and Learning Methods to Task and Domain Characteristics: Towards an Analytic Framework

A particular strength of case-based reasoning (CBR) over most other methods is its inherent combination of problem solving with sustained learning through problem solving experience. This is therefore a particularly important topic of study, and an issue that has now become mature enough to be addressed in a more systematic way. To enable such an analysis of problem solving and learning, we have initiated work towards the development of an analytic framework for studying CBR methods. It provides an explicit ontology of basic CBR task types, domain characterisations, and types of problem solving and learning methods. Further, it incorporates within this framework a methodology for combining a knowledge-level, top-down analysis with a bottom-up, case-driven one. In this article, we present the underlying view and the basic approach being taken, the main components of the framework and accompanying methodology, examples of studies recently done and how they relate to the framework.

[1]  E. R. Bareiss,et al.  Protos: An Exemplar-Based Learning Apprentice , 1988, Int. J. Man Mach. Stud..

[2]  M. Manago,et al.  Induction and Reasoning from Cases , 1993 .

[3]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[4]  Agnar Aamodt,et al.  Different Roles and Mutual Dependencies of Data, Information, and Knowledge - An AI Perspective on their Integration , 1995, Data Knowl. Eng..

[5]  Klaus-Dieter Althoff.  Eine fallbasierte Lernkomponente als integraler Bestandteil der MOLTKE-Werkbank zur Diagnose technischer Systeme , 1993, DISKI.

[6]  Agnar Aamodt,et al.  Knowledge acquisition and learning by experience—the role of case-specific knowledge , 1995 .

[7]  Klaus Goos,et al.  Fallbasiertes Klassifizieren - Methoden, Integration und Evaluation , 1996, DISKI.

[8]  Enric Plaza,et al.  Case-Based Learning of Strategic Knowledge , 1991, EWSL.

[9]  Klaus-Dieter Althoff,et al.  Decision Support for Case-Based Applications , 1996, Wirtsch..

[10]  Agnar Aamodt,et al.  Explanation-Driven Case-Based Reasoning , 1993, EWCBR.

[11]  Luc Steels,et al.  Components of Expertise , 1990, AI Mag..

[12]  Agnar Aamodt,et al.  A knowledge-intensive, integrated approach to problem solving and sustained learning , 1992 .

[13]  Michel Manago,et al.  Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations , 1994, EWCBR.

[14]  E. R. Bareiss,et al.  Protos: An Exemplar-Based Learning Apprentice1 , 1987 .

[15]  Michel Manago,et al.  Advances in Case-Based Reasoning , 1994, Lecture Notes in Computer Science.

[16]  Eva Armengol,et al.  A Knowledge Level Model of Case-Based Reasoning , 1993, EWCBR.

[17]  Reid Simmons,et al.  Second Generation Expert Systems , 1993, Springer Berlin Heidelberg.

[18]  Stefan Wess,et al.  Similarity, Uncertainty and Case-Based Reasoning in Patdex , 1991, Automated Reasoning: Essays in Honor of Woody Bledsoe.

[19]  Guus Schreiber,et al.  Towards a unification of knowledge modelling approaches , 1993 .

[20]  Agnar Aamodt,et al.  Case-Based Reasoning Research and Development , 1995, Lecture Notes in Computer Science.

[21]  Koton Phyllis,et al.  Using experience in learning and problem solving , 1988 .

[22]  Stefan Weß.  Patdex - ein Ansatz zur wissensbasierten und inkrementellen Verbesserung von Ähnlichkeitsbewertungen in der fallbasierten Diagnostik , 1993, XPS.

[23]  Robert S. Boyer,et al.  Automated Reasoning: Essays in Honor of Woody Bledsoe , 1991, Automated Reasoning.

[24]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..