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Modellbasierte Diagnose

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Expertensysteme

Part of the book series: Springers Angewandte Informatik ((SPINFO))

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Zusammenfassung

MYCIN und fast alle anderen bisher entwickelten Expertensysteme sind Vertreter des sogenannten „shallow reasoning“. In diesen Expertensystemen sind Fehler-Ursache-Beziehungen in Form von heuristischen Regeln oder ähnlichen Repräsentationsformen dargestellt. Die Funktion und Struktur des zu diagnostizierenden Systems ist nicht formal repräsentiert. Obwohl dieser Ansatz teilweise in der Lage ist, das Verhalten eines Experten nachzuahmen, liegen die Schwierigkeiten vor allem in der Erweiterbarkeit, Korrektheit und Vollständigkeit der heuristischen Wissensbasis. In größeren Systemen ist es im allgemeinen schwierig, die Diagnose-Wissensbasis zu überblicken bzw. zu erweitern, da viele Regeln voneinander abhängen und daher der Effekt einer neu eingefügten Regel schwer zu beurteilen ist.

“When you have eliminated the impossible, whatever remains, however improbable, must be the truth.”

— Sherlock Holmes. The Sign of the Four.

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Literatur

  1. Randall, D. (1984): Diagnostic reasoning based on structure and behaviour. Artificial Intelligence 224: 347–410.

    Google Scholar 

  2. Randall, D., Hamscher, W. (1988): Model-based reasoning: Troubleshooting. In: Exploring Artificial Intelligence, chapter 8. Morgan Kaufmann, Los Altos, LA, pp. 297–346.

    Google Scholar 

  3. Kleer, J. de (1986): An assumption-based TMS. Artificial Intelligence 28: 127–162.

    Article  Google Scholar 

  4. Kleer, J. de, Williams, B. C. (1987): Diagnosing multiple faults. Artificial Intelligence 32: 97–130.

    Article  MATH  Google Scholar 

  5. Kleer, J. de, Williams, B. C. (1989): Diagnosis with behavioral modes. In: Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, August 1989. Morgan Kaufmann, Los Altos, CA, pp. 1324–1330.

    Google Scholar 

  6. Friedrich, G., Gottlob, G., Nejdl, W. (1990): Physical impossibility instead of fault models. In: Proceedings of the National Conference on Artificial Intelligence, Boston, August 1990.

    Google Scholar 

  7. Friedrich, G., Nejdl, W. (1990): MOMO — Model-based diagnosis for everybody. In: Proceedings of the IEEE Conference on Artificial Intelligence Applications, Santa Barbara, March 1990.

    Google Scholar 

  8. Forbus, K. D. (1988): Qualitative physics: Past, present, and future. In: Exploring Artificial Intelligence, chapter 7. Morgan Kaufmann, Los Altos, CA, pp. 239–296.

    Google Scholar 

  9. Genesereth, M. R. (1984): The use of design descriptions in automated diagnosis. Artificial Intelligence 24: 411–436.

    Article  Google Scholar 

  10. Manthey, R., Bry, F. (1987): A hyperresolution-based proof procedure and its implementation in Prolog. In: Proceedings of the German Workshop on Artificial Intelligence, pp. 221–230.

    Google Scholar 

  11. Raiman, O. (1989): Diagnosis as trial — the alibi principle. In: International Model-Based Diagnosis Workshop, Paris, July 1989.

    Google Scholar 

  12. Reiter, R. (1987): A theory of diagnosis from first principles. Artificial Intelligence 32: 57–95.

    Article  MATH  MathSciNet  Google Scholar 

  13. Struss, P., Dressler, O. (1989): Physical negation — Integrating fault models into the general diagnostic engine. In: Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, August 1989. Morgan Kaufmann, Los Altos, CA, pp. 1318 —1323.

    Google Scholar 

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© 1990 Springer-Verlag/Wien

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Nejdl, W. (1990). Modellbasierte Diagnose. In: Gottlob, G., Frühwirth, T., Horn, W. (eds) Expertensysteme. Springers Angewandte Informatik. Springer, Vienna. https://doi.org/10.1007/978-3-7091-9094-4_9

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  • DOI: https://doi.org/10.1007/978-3-7091-9094-4_9

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82221-0

  • Online ISBN: 978-3-7091-9094-4

  • eBook Packages: Springer Book Archive

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