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

Excerpt

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 readily-available 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. …