Using KATE for case-based reasoning in maintenance

Induction and case-based reasoning (CBR) are two technologies that have recently received considerable attention. A key distinction between CBR and other forms of automated reasoning, including induction, is that, in case-based methods, a new problem is solved by recognizing its similarities to a specific known problem then transferring the solution of the known problem to new one. In contrast, other methods of problem solving derive a solution either from a general characterisation of a group of problems or by search through a still more general body of knowledge. According to this, we distinguish between a pure inductive approach and case-based one because induction first computes an abstraction of the case database (e.g. a decision tree or a set or rules) and then uses this general knowledge for problem solving. We present two components of the KATE-toolbox for reasoning with cases, KATE induction that generates decision trees and/or decision rules from cases, and KATE-CBR that supports case based reasoning activity.<>