Supporting model-based diagnosis with explanation-based learning and analogical inferences

  • Dieter Specht
  • Sabine Weiß
Machine Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 604)


This paper introduces two learning approaches used in different phases of a diagnostic system's life cycle. First, an initial knowledge-base is built using an explanation-based learning approach which generates diagnostic rules. A functional model of the object to be diagnosed constitutes the necessary domain knowledge. Later when the system is operational, analogical inferences which utilize taxonomic information continue to improve its diagnostic performance. In this way knowledge which is ‘objectivized’ by the model can be acquired, greatly improving the performance of a pure model-based diagnosis while preserving the advantages of the model-based approach.


analogical inference explanation-based learning machine learning model-based diagnosis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Dieter Specht
    • 1
    • 2
  • Sabine Weiß
    • 1
    • 2
  1. 1.Fraunhofer Institute for Production Systems and Design TechnologyGermany
  2. 2.Institute for Machine Tools and Manufacturing TechnologyTechnical University BerlinBerlin 10Germany

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