Guest editors’ introduction: special issue on case-based reasoning

Abstract

This Special Issue contains three articles providing samples of current case-based reasoning (CBR) research. Case-based reasoning systems perform problem-solving and interpretation based on a library of prior cases, called the case base. Given a new problem, CBR systems generate solutions by retrieving the most relevant prior case(s) in the case base, and adapting their solutions to fit new circumstances. Each new solution then becomes the basis of a new case, available to be stored. The capture of new cases can provide speedup learning by making available new cases, which require less adaptation for similar problems. In addition, the capture of new cases can increase competence if the application of a solution reveals flaws, which are then corrected to form a new case avoiding the problems. By leveraging readilyavailable prior experiences, rather than relying on extracting complex (and hard to come by) domain knowledge, case-based reasoning has enjoyed success in many application contexts and has been widely applied, giving rise to numerous fielded applications. The articles in this issue illustrate three important directions in CBR research. The primary locus of “reasoning” in CBR is the case adaptation step, but a classic problem for CBR is how to acquire the knowledge required to perform that case adaptation. One of the responses of the CBR community has been to apply machine learning methods to generating case adaptation knowledge. “Enhancing Case-Based Regression with Automatically-Generated Ensembles of Adaptations,” by Jalali and Leake, shows how the ability to generate adaptation rules can be leveraged by the application of rule ensembles.