Dynamic Domain Abstraction Through Meta-diagnosis

  • Johan de Kleer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4612)


One of the most powerful tools designers have at their disposal is abstraction. By abstracting from the detailed properties of a system, the complexity of the overall design task becomes manageable. Unfortunately, faults in a system need not obey the neat abstraction levels of the designer. This paper presents an approach for identifying the abstraction level which is as simple as possible yet sufficient to address the task at hand. The approach chooses the desired abstraction level through applying model-based diagnosis at the meta-level, i.e., to the abstraction assumptions themselves.


Abstraction diagnosis qualitative reasoning model-based reasoning 


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  1. 1.
    Struss, P.: What’s in SD? Towards a theory of modeling for diagnosis. In: Console, L., (ed.) Working Notes of the 2st Int. Workshop on Principles of Diagnosis. Technical Report RT/DI/91-10-7, Dipartimento di Informatica, Universitá di Torino, Torino, Italy, pp. 41–51 (1991)Google Scholar
  2. 2.
    Struss, P.: A theory of model simplification and abstraction for diagnosis. In: Proc. 5th Int. Workshop on Qualitative Physics, Austin, TX (1991)Google Scholar
  3. 3.
    Sachenbacher, M., Struss, P.: Task-dependent qualitative domain abstraction. Artif. Intell. 162(1-2), 121–143 (2005)CrossRefGoogle Scholar
  4. 4.
    Torta, G., Torasso, P.: Automatic abstraction in component-based diagnosis driven by system observability. In: Gottlob, G., Walsh, T. (eds.) IJCAI, pp. 394–402. Morgan Kaufmann, San Francisco (2003)Google Scholar
  5. 5.
    Chittaro, L., Ranon, R.: Hierarchical model-based diagnosis based on structural abstraction. Artif. Intell. 155(1-2), 147–182 (2004)zbMATHCrossRefGoogle Scholar
  6. 6.
    Hamscher, W.C.: XDE: Diagnosing devices with hierarchic structure and known component failure modes. In: Proc. 6th IEEE Conf. on A.I. Applications, Santa Barbara, CA, pp. 48–54. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
  7. 7.
    Addanki, S., Cremonini, R., Penberthy, J.S.: Reasoning about assumptions in graphs of models. In: Proc. 11th Int. Joint Conf. on Artificial Intelligence, Detroit, MI, pp. 1432–1438 (1989)Google Scholar
  8. 8.
    Falkenhainer, B., Forbus, K.D.: Compositional modeling: Finding the right model for the job. Artificial Intelligence 51 95–143 (1991), Also In: de Kleer, J., Williams, B. (eds.) Qualitative Reasoning about Physical Systems II, pp. 95–143. North-Holland 1991/ MIT Press 1992, Amsterdam/Cambridge (1992)Google Scholar
  9. 9.
    de Kleer, J., Williams, B.C.: Diagnosing multiple faults. Artificial Intelligence 32 97–130 (1987), Also In: Ginsberg, M.L. (ed.) Readings in NonMonotonic Reasoning, pp. 280–297. Morgan Kaufmann, San Francisco (1987)Google Scholar
  10. 10.
    de Kleer, J., Mackworth, A., Reiter, R.: Characterizing diagnoses and systems. Artificial Intelligence 56(2-3), 197–222 (1992)CrossRefGoogle Scholar
  11. 11.
    de Kleer, J.: Modeling when connections are the problem. In: Proc. 20th IJCAI, Hyderabad, India, pp. 311–317 (2007)Google Scholar
  12. 12.
    Böttcher, C.: No faults in structure? how to diagnose hidden interactions. In: IJCAI, pp. 1728–1735 (1995)Google Scholar
  13. 13.
    Böttcher, C., Dague, P., Taillibert, P.: Hidden interactions in analog circuits. In: Abu-Hakima, S. (ed.) Working Papers of the Seventh International Workshop on Principles of Diagnosis, Val Morin, Quebec, Canada, pp. 36–43 (1996)Google Scholar
  14. 14.
    Raiman, O., de Kleer, J., Saraswat, V., Shirley, M.H.: Characterizing non-intermittent faults. In: Proc. 9th National Conf. on Artificial Intelligence, Anaheim, CA, pp. 849–854 (1991)Google Scholar
  15. 15.
    de Kleer, J.: Diagnosing intermittent faults. In: 18th International Workshop on Principles of Diagnosis, Nashville, USA, pp. 45–51 (2007)Google Scholar
  16. 16.
    de Kleer, J.: Troubleshooting temporal behavior in combinational circuits. In: 18th International Workshop on Principles of Diagnosis, Nashville, USA, pp. 52–58 (2007)Google Scholar
  17. 17.
    Wikipedia: Ring oscillator — Wikipedia, the free encyclopedia (2007) (Online, accessed February 12, 2007)Google Scholar
  18. 18.
    de Kleer, J.: A hybrid truth maintenance system. PARC Technical Report (January 1992)Google Scholar
  19. 19.
    Nayak, P.P. (ed.): Automated Modeling of Physical Systems. LNCS, vol. 1003. Springer, Heidelberg (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Johan de Kleer
    • 1
  1. 1.Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304USA

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