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References

  1. J.S. Brown, R.R. Burton and J. de Kleer, Pedagogical, natural language and engineering techniques in SOPHIE I, II and III, in:Intelligent Tutoring Systems, eds. D. Sleeman and J.S. Brown (Academic Press, 1982); also in [25].

  2. T. Bylander, D. Allemang, M. Tanner and J. Josephson, The computational complexity of abduction, Artificial Intelligence 49(1–3) (1991) 25–60.

    Google Scholar 

  3. K. Clark, Negation as failure, in:Logic and Data Bases, eds. H. Gallaire and J. Minker (Plenum Press, 1978) pp. 293–322.

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

    Google Scholar 

  5. L. Console and P. Torasso, A spectrum of logical definitions of model-based diagnosis, Computational Intelligence 7(3) (1991) 133–141; also in [25].

    Google Scholar 

  6. P.T. Cox and T. Pietrzykowski, General diagnosis by abductive inference,Proc. IEEE Symp. Logic Programming, San Francisco, 1987, pp. 183–189.

  7. P. Dague, P. Deves, P. Luciani and P. Taillibert, Analog systems diagnosis,Proc. 9th ECAI, Stockholm, 1990, pp. 173–178; also in [25].

  8. P. Dague, O. Jehl, P. Deves, P. Luciani and P. Taillibert, When oscillators stop oscillating,Proc. 12th IJCAI, Sydney, 1991, pp. 1109–1115; also in [25].

  9. R. Davis, Diagnostic reasoning based on structure and behavior, Artificial Intelligence 24(1–3) (1984) 347–410.

    Google Scholar 

  10. R. Davis and W. Hamscher, Model-based reasoning: Troubleshooting, in:Exploring Artificial Intelligence, ed. H.E. Shrobe (Morgan Kaufman, 1988) pp. 297–346; also in [25].

  11. J. de Kleer, Using crude probability estimates to guide diagnosis, Artificial Intelligence 45(3) (1990) 381–391; also in [25].

    Google Scholar 

  12. J. de Kleer, Focusing on probable diagnoses,Proc. AAAI91, Anaheim, CA, 1991, pp. 842–848; also in [25].

  13. J. de Kleer, A. Mackworth, and R. Reiter, Characterizing diagnoses and systems, Artificial Intelligence 56(2–3) (1992) 197–222; also in [25].

    Google Scholar 

  14. J. de Kleer and B.C. Williams, Diagnosing multiple faults, Artificial Intelligence 32(1) (1987) 97–130; also in [25].

    Google Scholar 

  15. D. DeCoste, Dynamic across-time measurement interpretation, Artificial Intelligence 51(1–3) (1991) 273–341.

    Google Scholar 

  16. D. Dvorak and B. Kuipers, Model-based monitoring of dynamic systems,Proc. 11th IJCAI, Detroit, 1989, pp. 1238–1243; also in [25].

  17. K. Eshghi, Computing stable models by using the ATMS.Proc. AAAI90, Boston, 1990, pp. 272–276.

  18. G. Friedrich, G. Gottlob and W. Nejdl, Physical impossibility instead of fault models,Proc. AAAI90, Boston, 1990, pp. 331–336; also in [25].

  19. G. Friedrich and F. Lackinger, Diagnosing temporal misbehaviour,Proc. 12th IJCAI, Sydney, 1991, pp. 1116–1122.

  20. G. Friedrich, G. Gottlob, and W. Nejdl, Hypothesis classification, abductive diagnosis and therapy,Proc. Int. Workshop on Expert Systems in Engineering, Vienna, 1990, Lecture Notes in Artificial Intelligence, Vol. 462 (Springer Verlag).

  21. H. Geffner and J. Pearl, Distributed diagnosis of systems with multiple faults,Proc 3rd IEEE Conf. on AI Application, Orlando, 1987, pp. 156–162; also in [25].

  22. M. Gelfond and V. Lifschitz, The stable model semantics for logic programming,Proc. 5th Int. Conf. and Symp. on Logic Programming, Seattle, 1988, pp. 1070–1080.

  23. M.R. Genesereth, The use of design descriptions in automated diagnosis, Artificial Intelligence 24(1–3) (1984) 411–436; also in [25].

    Google Scholar 

  24. W. Hamscher, Modeling digital circuits for troubleshooting, Artificial Intelligence 51(1–3) (1991) 223–271; also in [25].

    Google Scholar 

  25. W. Hamscher, L. Console and J. de Kleer,Readings in Model-Based Diagnosis (Morgan Kaufmann, 1992).

  26. W. Hamscher and R. Davis, Diagnosing circuit with state: an inherently underconstrained problem,Proc. AAAI84, Austin, 1984, pp. 142–147; also in [25].

  27. P. Jackson, Non-monotonic formalisms and diagnosis, Knowledge Eng. Rev. 4(4) (1989) 97–117.

    Google Scholar 

  28. K. Konolige, Abduction versus closure in causal theories, Artificial Intelligence 53(2–3) (1992) 255–272.

    Google Scholar 

  29. F. Lackinger and W. Nejdl, Integrating model-based monitoring and diagnosis of complex dynamic systems,Proc. 12th IJCAI, Sydney, 1991, pp. 1123–1128.

  30. J. McCarthy, Circumscription: a form of non-monotonic reasoning, Artificial Intelligence 13(1–2) (1980) 27–39.

    Google Scholar 

  31. I. Mozetič, Hierarchical model-based diagnosis, Int. J. Man-Machine Studies 35(3) (1991) 329–362; also in [25].

    Google Scholar 

  32. W. Nejdl, Belief revision, diagnosis and repair,GI-KB, München, 1991 (Springer Verlag).

  33. H.T. Ng, Model-based, multiple fault diagnosis of dynamic, continuous physical devices, IEEE Expert 6(6) (1991) 38–43; also in [25].

    Google Scholar 

  34. R. Patil, Causal representation of patient illness for electrolyte and acid-base diagnosis, Technical Report LCS-267, MIT, Cambridge, MA (1981).

    Google Scholar 

  35. J. Pearl,Probabilistic Reasoning in Intelligent Systems (Morgan Kaufmann, 1989).

  36. D. Poole, A logical framework for default reasoning, Artificial Intelligence 36(1) (1988) 27–47.

    Google Scholar 

  37. D. Poole, Normality and faults in logic-based diagnosis,Proc. 11th IJCAI, Detroit, 1989, pp. 1304–1310; also in [25].

  38. D. Poole, R. Goebel and R. Aleliunas, THEORIST: A logical reasoning system for defaults and diagnosis, in:The Knowledge Frontier, eds. N. Cercone and G. McCalla (Springer Verlag, 1987) pp. 331–352.

  39. R. Reiter, A theory of diagnosis from first principles, Artificial Intelligence 32(1) (1987) 57–96; also in [25].

    Google Scholar 

  40. B. Selman and H. Levesque, Abductive and default reasoning: A computational core,Proc. AAAI90, Boston, 1990, pp. 343–348.

  41. P. Struss, Diagnosis as a process,Working Notes of 1st Int. Workshop on Model-based Diagnosis, Paris, 1989; also in [25].

  42. P. Struss, Knowledge-based diagnosis: an important challenge and touchstone for AI,Proc. 10th ECAI, Vienna, 1992, pp. 863–874.

  43. S. Weiss, C. Kulikowski, S. Amarel and A. Safir, A model based method for computer-aided medical decision making, Artificial Intelligence 11(1–2) (1978) 145–172.

    Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Informatica, Università di Torino, Corso Svizzera 185, 10149, Torino, Italy

    Luca Console

  2. Engineering Center for Electronics, SIEMENS AG Austria, Erdberger Lände 26, A-1031, Vienna, Austria

    Gerhard Friedrich

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  1. Luca Console
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Console, L., Friedrich, G. Introduction. Ann Math Artif Intell 11, 1–10 (1994). https://doi.org/10.1007/BF01530734

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