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Controlling the complexity in model-based diagnosis

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Abstract

We present IDA — an incrementaldiagnosticalgorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, the worst-case complexity of the algorithm to compute thek+1st minimal diagnosis isO(n 2k), wheren is the number of components. On the practical side, an experimental evaluation indicates that the algorithm can efficiently diagnose devices consisting of a few thousand components. We propose to use a hierarchy of models: first a structural model to compute all minimal diagnoses, then a normal behavior model to find the additional diagnoses if needed, and only then a fault model for their verification. IDA separates model interpretation from the search for minimal diagnoses in the sense that the model interpreter is replaceable. In particular, we show that in some domains it is advantageous to use the constraint logic programming system CLP(ß) instead of a logic programming system like Prolog.

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References

  1. R.R. Bakker, P.A. Hogenhuis, N.J.I. Mars and D.C. van Soest, Diagnosis of technical systems using design descriptions,Proc. Second Generation Expert Systems, Avignon, France, 1989, pp. 107–116.

  2. G. Boole,The Mathematical Analysis of Logic (Macmillan, 1947).

  3. I. Bratko, I. Mozetič and N. Lavrač,KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, Cambridge, MA, 1989).

    Google Scholar 

  4. W. Büttner and H. Simonis, Embedding Boolean expressions into logic programming, J. Symbolic Comput. 4 (1987) 191–205.

    Google Scholar 

  5. T. Bylander, D. Allemang, M.C. Tanner and J.R. Josephson, Some results concerning the computational complexity of abduction,Proc. Int. Conf. on Principles of Knowledge Representation and Reasoning, Toronto (Morgan Kaufmann, 1989) pp. 44–54.

  6. M. Carlsson and J. Widen, SICStus Prolog User's Manual, Swedish Institute of Computer Science, Kista, Sweden (1991).

    Google Scholar 

  7. J. Cohen, Constraint logic programming languages, Commun. ACM 33(7) (1990) 52–68.

    Google Scholar 

  8. B. Crone-Rawe, Unification algorithms for boolean rings, SEKI Working Paper SWP-89-01, University of Kaiserslautern, Germany (1989).

    Google Scholar 

  9. J. de Kleer, Focusing on probable diagnoses,Proc. 9th Natl. Conf. on Artificial Intelligence, AAAI-91, Anaheim, CA (MIT Press, 1991) pp. 842–848.

  10. J. de Kleer, A.K. Mackworth and R. Reiter, Characterizing diagnoses,Proc. 8th Natl. Conf. on Artificial Intelligence, AAAI-90, Boston (MIT Press, 1990) pp. 324–330.

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

    Google Scholar 

  12. J. de Kleer and B.C. Williams, Diagnosis with behavioral modes,Proc. 11th Int. Joint Conf. on Artificial Intelligence, IJCAI-89, Detroit (Morgan Kaufmann, 1989) pp. 1324–1330.

  13. G. Friedrich, G. Gottlob and W. Nejdl, Physical impossibility instead of fault models,Proc. 8th Natl. Conf. on Artificial Intelligence, AAAI-90, Boston (MIT Press, 1990) pp. 331–336.

  14. G. Friedrich, G. Gottlob and W. Nejdl, Formalizing the repair process,Proc. 10th European Conf. on Artificial Intelligence, ECAI-92, Vienna (Wiley, 1992) pp. 709–713.

  15. G. Friedrich and W. Nejdl, MOMO: Model-based diagnosis for everybody,Proc. 6th IEEE Conf. on AI Applications, CAIA-90, Santa Barbara, CA (IEEE Press, 1990) pp. 206–213.

  16. M. Gallanti, M. Roncato, A. Stefanini and G. Tornielli, A diagnostic algorithm based on models at different level of abstraction,Proc. 11th Int. Joint Conf. on Artificial Intelligence, IJCAI-89, Detroit (Morgan Kaufmann, 1989) pp. 1350–1355.

  17. M.R. Garey and D.S. Johnson,Computers and Intractability (Freeman, New York, 1979).

    Google Scholar 

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

    Google Scholar 

  19. F. Giunchiglia and T. Walsh, Abstract theorem proving,Proc. 11th Int. Joint Conf. on Artificial Intelligence, IJCAI-89, Detroit, MI (Morgan Kaufmann, 1989) pp. 372–377.

  20. C. Holzbaur, Specification of constraint based inference mechanisms through extended unification, Ph.D. Thesis, Dept. of Medical Cybernetics and AI, University of Vienna, Austria (1990).

    Google Scholar 

  21. J. Jaffar and J.-L. Lassez, Constraint logic programming,Proc. 14th ACM Symp. on Principles of Programming Languages, Munich, 1987, pp. 111–119.

  22. J.W. Lloyd,Foundation of Logic Programming, 2nd Ed. (Springer, 1987).

  23. U. Martin and T. Nipkov, Unification in boolean rings,Proc. 8th Int. Conference on Automated Deduction, 1986, pp. 506–513.

  24. I. Mozetič, Hierarchical model-based diagnosis, Int. J. Man-Machine Stud. 35 (3) (1991) 329–362. Also in: W.C. Hamscher, L. Console and J. de Kleer (eds.),Readings in Model-based Diagnosis (Morgan Kaufmann, San Mateo, CA, 1992) pp. 354–372.

    Google Scholar 

  25. I. Mozetič, C. Holzbaur, F. Novak and M. Santo-Zarnik, Model-based analogue circuit diagnosis with CLP(5R),Proc. 4th Int. GI Congress, Munich (Springer, 1991) pp. 343–353.

  26. I. Mozetič and B. Pfahringer, Improving diagnostic efficiency in KARDIO: abstractions, constraint propagation, and model compilation, in: E. Keravnou (ed.),Deep Models for Medical Knowledge Engineering (Elsevier, Amsterdam, 1992) pp. 1–25.

    Google Scholar 

  27. F. Novak, I. Mozetič, M. Santo-Zarnik and A. Biasizzo, Enhancing design-for-test for active analog filters by using CLP(ℜ), Analog Integrated Circuits and Signal Processing 4(3) (1993) 215–229. Also in: J. Electronic Testing 4 (1993) 315–329.

    Google Scholar 

  28. D. Poole, Normality and faults in logic-based diagnosis,Proc. 11th Int. Joint Conf. on Artificial Intelligence, IJCAI-89, Detroit (Morgan Kaufmann, 1989) pp. 1304–1310.

  29. O. Raiman, Diagnosis as a trial: the alibi principle, Report, IBM Scientific Center, Paris (1989).

    Google Scholar 

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

    Google Scholar 

  31. V.A. Saraswat, J. de Kleer and O. Raiman, Contributions to a theory of diagnosis,Proc. First Int. Workshop on Principles of Diagnosis, Stanford University, Palo Alto, 1990, pp. 33–38.

    Google Scholar 

  32. P. Strass, What's in SD? Towards a theory of modeling for diagnosis, in: W.C. Hamscher, L. Console and J. de Kleer (eds.),Readings in Model-based Diagnosis (Morgan Kaufmann, San Mateo, CA, 1992) pp. 419–449.

    Google Scholar 

  33. P. Struss and O. Dressler, “Physical negation” — integrating fault models into the general diagnostic engine,Proc. 11th Int. Joint Conf. on Artificial Intelligence, IJCAI-89, Detroit (Morgan Kaufmann, 1989) pp. 1318–1323.

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Authors and Affiliations

  1. Department of Information Systems, Technical University of Vienna, and Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Igor Mozetič

  2. Austrian Research Institute for Artificial Intelligence, and Department of Medical Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6, A-1010, Vienna, Austria

    Christian Holzbaur

Authors
  1. Igor Mozetič
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  2. Christian Holzbaur
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Additional information

This is an extended version of the paper by Igor Mozetič, “A polynomial-time algorithm for model-based diagnosis,” which appears in theProc. European Conf. on Artificial Intelligence, ECAI-92, ed. B. Neumann (Wiley, 1992) pp. 729–733.

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Mozetič, I., Holzbaur, C. Controlling the complexity in model-based diagnosis. Ann Math Artif Intell 11, 297–314 (1994). https://doi.org/10.1007/BF01530747

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