Using CBR as Design Methodology for Developing Adaptable Decision Support Systems

Although knowledge-based systems (KBS), and more generally decision support systems (DSS), represent one of the commercial successes resulting from artificial intelligence (AI) research, their developers have repeatedly encountered several problems covering their whole life cycle (Watson, 1997). In this context, knowledge elicitation as well as system implementation, adaptation and maintenance are non trivial issues to be dealt with. With the aim of overcoming these problems, Schank (1982) proposed a revolutionary approach called case-based reasoning (CBR), which is in effect, a model of human reasoning. The idea underlying CBR is that people frequently rely on previous problem-solving experiences when facing up new problems. This assertion may be verified in many day to day problem-solving situations by simple observation or by psychological experimentation (Klein & Whitaker, 1988). Since the ideas underlying case-based reasoning were first established, CBR systems have been found to be successful in a wide range of application areas (Kolodner, 1993; Watson, 1997; Pal et al. 2000). Motivated by the outstanding achievements obtained, some relevant conferences (i.e. ECCBR1 and ICCBR2) and international journals (e.g.: International Journal Transactions on Case-Based Reasoning) have successfully grown up in the field. In this chapter we present key aspects related with the application of CBR methodology to the construction of adaptable decision support systems. The rest of the chapter is organized as follows: Section 2 introduces an overview about CBR life cycle and combination strategies for constructing hybrid AI systems. Section 3 introduces and covers the main characteristics of four successful decision support systems developed following CBR principles. Finally, Section 4 summarizes the main conclusions and presents the fundamental advantages of adopting this methodology.

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