Resource title

Case-based reasoning systems: from automation to decision-aiding and stimulation

Resource image

image for OpenScout resource :: Case-based reasoning systems: from automation to decision-aiding and stimulation

Resource description

Case-based reasoning (CBR) has emerged as a major research area within artificial intelligence research over the last decade due to its widespread usage by humans and its appeal as a methodology for building intelligent systems. Conventional CBR systems have been designed as automated problem solvers for producing a solution to a given problem by adapting the solution to a similar, previously solved problem. Such systems have had a limited success in real-world applications. Recently, there has been a search for new paradigms and directions for increasing the utility of CBR systems for decision support. This paper focuses on the synergy between the research areas of decision support systems (DSSs) and CBR. A conceptual framework for DSSs is presented and used to develop a taxonomy of three different types of CBR systems: conventional, decision-aiding and stimulative. The major characteristics of each type of CBR system are explained with a focus on decision-aiding and stimulative CBR systems

Resource author

Resource publisher

Resource publish date

Resource language

en

Resource content type

application/pdf

Resource resource URL

http://flora.insead.edu/fichiersti_wp/inseadwp1996/96-67.pdf

Resource license

Copyright INSEAD. All rights reserved