Using case-based reasoning to focus model-based diagnostic problem solving

  • Luigi Portinale
  • Pietro Torasso
  • Carlo Ortalda
  • Antonio Giardino
Selected Papers Integrated Architectures
Part of the Lecture Notes in Computer Science book series (LNCS, volume 837)


The aim of this paper is to present an approach to the integration of Case-Based Reasoning with Model-Based Reasoning in diagnostic problem solving. Such an integration is exploited by defining adaptation criteria on solutions retrieved by a case-based reasoner, in order to focus the model-based reasoner in the search for the solution of the current case and avoiding, as much as possible, the computation of the solution from scratch. Such adaptation criteria strictly rely on a formad theory of diagnosis that allows us to define different adaptation levels, relative to the trade-off between “accuracy of the solution” and “computational effort”. A simple example in the domain of car engine faults is presented and some important aspects are finally pointed out on the basis of our preliminary experiments.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    D.S. Aghassi. Evaluating case-based reasoning for heart failure diagnosis. Technical report, Dept. of EECS, MIT, Cambridge, MA, 1990.Google Scholar
  2. 2.
    K.D. Ashley and E.L. Rissland. Compar and contrast, a test of expertise. In Proc. 6th AAAI, pages 273–278, Seattle, 1987.Google Scholar
  3. 3.
    P.P. Bonissone and S. Dutta. Integrating case-based and rule-based reasoning: the possibilistic connection. In Proc. 6th Conf. on Uncertainty in Artificial Intelligence, Cambridge, MA, 1990.Google Scholar
  4. 4.
    L. Console, L. Portinale, and D. Theseider Dupré. Focusing abductive diagnosis. In Proc. 11th Int. Conf. on Expert Systems and Their Applications (Conf. on 2nd Generation Expert Systems), pages 231–242, Avignon, 1991. Also in AI Communications 4(2/3):88–97, 1991.Google Scholar
  5. 5.
    L. Console, L. Portinale, D. Theseider Dupré, and P. Torasso. Combining heuristic and causal reasoning in diagnostic problem solving. In J.M. David, J.P. Krivine, and R. Simmons, editors, Second Generation Expert Systems, pages 46,68. Springer Verlag, 1993.Google Scholar
  6. 6.
    L. Console and P. Torasso. A spectrum of logical definitions of model-based diagnosis. Computational Intelligence, 7(3):133–141, 1991.Google Scholar
  7. 7.
    J.M. David, J.P. Krivine, and R. Simmons (eds.). Second Generation Expert Systems. Springer Verlag, 1993.Google Scholar
  8. 8.
    J. de Kleer. Focusing on probable diagnoses. In Proc. AAAI 91, pages 842–848, Anaheim, CA, 1991. Also in [11].Google Scholar
  9. 9.
    A. Goel. Integration of case-based reasoning and model-based reasoning for adaptive design problem solving. Technical report, (PhD Diss.) Dept. of Comp. and Inf. Science, Ohio Univ., 1989.Google Scholar
  10. 10.
    K.J.Hammond. Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, 1989.Google Scholar
  11. 11.
    W. Hamscher, L. Console, and J. de Kleer. Readings in Model-Based Diagnosis. Morgan Kaufmann, 1992.Google Scholar
  12. 12.
    Y. Jang. HYDI: a hybrid system with feedback for diagnosing multiple disorders. Technical report, MIT/LCS/TR-576, 1993.Google Scholar
  13. 13.
    E.K. Jones. Model-based case adaptation. In Proc. AAAI 92, pages 673–678, San Jose', 1992.Google Scholar
  14. 14.
    J. Kolodner and R. Kolodner. Using experience in clinical problem solving: Introduction and framework. IEEE Trans. on Systems, Man and Cybernetics, 17(3):420–431, 1987.Google Scholar
  15. 15.
    J.L. Kolodner. Retrieval and Organization Strategies in Conceptual Memory: a Computer Model. Lawrence Erlbaum, 1984.Google Scholar
  16. 16.
    P. Koton. Using experience in learning and problem solving. Technical report, MIT/LCS/TR-441, 1989.Google Scholar
  17. 17.
    D. Macchion and D.P. Vo. A hybrid KBS for technical diagnosis learning and assistance. In Proc. EWCBR 93, pages 307–312, Kaiserslautern, 1993.Google Scholar
  18. 18.
    G. Pews and S. Wess. Combining case-based and model-based approaches for diagnostic applications in technical domains. In Proc. EWCBR 93, pages 325–328, Kaiserslautern, 1993.Google Scholar
  19. 19.
    E.L. Rissland and D.B. Skalak. Combining case-based and rule-based reasoning: a heuristic approach. In Proc. 11th IJCAI, pages 524–530, Detroit, 1989.Google Scholar
  20. 20.
    P. Torasso and L. Console. Diagnostic Problem Solving: Combining Heuristic, Approximate and Causal Reasoning. Van Nostrand Reinhold, 1989.Google Scholar
  21. 21.
    P. Torasso, L. Portinale, L. Console, and M. Casassa Mont. Approximate reasoning in a system combining prototypical knowledge with case-based reasoning. In L.A. Zadeh and J. Kacprzyk, editors, Fuzzy Logic for the Management of Uncertainty. John Wiley & Sons, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Luigi Portinale
    • 1
  • Pietro Torasso
    • 1
  • Carlo Ortalda
    • 1
  • Antonio Giardino
    • 1
  1. 1.Dipartimento di InformaticaUniversita' di TorinoTorinoItaly

Personalised recommendations