Patdex is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench for technical diagnosis, which was developed at the university of Kaiserslautern over the past years, Moltke contains other parts as well, in particular a model-based approach; in Patdex where essentially the heuristic features are located. The use of cases also plays an important role for knowledge acquisition. In this paper we describe Patdex from a principal point of view and embed its main concepts into a theoretical framework 1 General Considerations Patdex 1 is an expert system which carries out case-based reasoning for the fault diagnosis of complex machines. It is integrated in the Moltke workbench 2 for technical diagnosis, which was developed at the university of Kaiserslautern over the past years (cf. e.g. [4, 5, 23]), Moltke contains other parts as well (cf. e.g. [16]), in particular a model-based approach (cf. [21, ...

Within this paper we focus on both the solution of real, complex problems using expert system technology and the acquisition of the necessary knowledge from a case-based reasoning point of view. The development of systems which can be applied to real world problems has to meet certain requirements. E.g., all available information sources have to be identified and utilized. Normally, this involves different types of knowledge for which several knowledge representation schemes are needed, because no scheme is equally natural for all sources. Facing empirical knowledge it is important to complement the use of manually compiled, statistic and otherwise induced knowledge by the exploitation of the intuitive understandability of case-based mechanisms. Thus, an integration of case-based and alternative knowledge acquisition and problem solving mechanisms is necessary. For this, the basis is to define the "role" which case-based inference can "play" within a knowledge acquisition wo...

We combine simple retrieval with domain-specific validation of retrieved cases to produce a useful practical tool for case-based reasoning. Based on 200 real-world cases, we retrieve between three and six cases over a wide range of new problems. This represents a selectivity ranging from 1.5% to 3%, compared to an average selectivity of only 11% from simple retrieval alone. c flDigital Equipment Corporation 1990. All rights reserved. 1 1 Introduction We have combined simple retrieval (based on the similarity of surface features) with domain-specific validation of retrieved cases to produce a useful practical tool for case-based reasoning. Starting with a case base of 200 real-world cases, we have narrowed our consideration to between three and six cases over a wide range of new problems. This represents a selectivity ranging from 1.5% to 3%, compared to an average selectivity of only 11% from this same case base using retrieval without validation. We are applying the same technol...