A logical approach to deal with incomplete causal models in diagnostic problem solving

  • Luca Console
  • Pietro Torasso
Logics For Artificial Intelligence
Part of the Lecture Notes in Computer Science book series (LNCS, volume 313)


Different approaches have been proposed in the last few years to the formalization of deep diagnostic problem solving. In this paper we briefly analyze the two main approaches proposed so far for modeling diagnostic problem solving, i.e. the logical one (a la Reiter or a la de Kleer) and the probabilistic one (a la Pearl). Starting from such an analysis, we propose a different approach to the formalization of deep reasoning, based on the adoption of peculiar forms of non-monotonic logics. The approach allows us to perform sophisticated and qualitative forms of "approximate" (common-sense) reasoning. More specifically, we have designed a formalism for deep modeling which permits the description (representation) of incomplete and/or uncertain causal knowledge. A formal logical semantics has been defined for the modeling scheme, so that all the deep reasoning process can be logically characterized and defined. In particular a complex form of hypothetical (assumption-based) reasoning scheme has been defined to deal with incomplete models.


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  1. [1]
    S. Andreassen, M. Woldbye, B. Falk, and S. Andersen, “MUNIN — A Causal probabilistic Network for Interpretation of Electromyographic Findings,” pp. 366–372 in Proc 10th IJCAI, Milano (1987).Google Scholar
  2. [2]
    D.G. Bobrow (ed), “Special issue on qualitative reasoning,” Artificial Intelligence 24(1984).Google Scholar
  3. [3]
    B. Chandrasekaran and R. Milne (eds), “Special Section on Reasoning about Structure, Behavior and Function,” Sigart Newsletter 93 pp. 4–55 (1985).Google Scholar
  4. [4]
    L. Console and P. Torasso, “Heuristic and Causal Reasoning in CHECK,” in to be presented at 12th IMACS World Conference on Scientific Computation 88, Paris (July 1988).Google Scholar
  5. [5]
    L. Console and P. Torasso, “Hypothetical Reasoning in Causal Models,” Techn. Rep. Univ. Torino (May 1987).Google Scholar
  6. [6]
    G. Cooper, “NESTOR: A computer-based medical diagnostic aid that integrates causal and probabilistic knowledge,” Ph.D. dissertation, Dept. of Computer Science, Stanford University (1984).Google Scholar
  7. [7]
    J. deKleer, “An Assumption-based TMS,” Artificial Intelligence 28 pp. 127–162 (1986).Google Scholar
  8. [8]
    J. deKleer and B.C. Williams, “Diagnosing Multiple Faults,” Artificial Intelligence 32 pp. 97–130 (1987).Google Scholar
  9. [9]
    D.M. Gabbay and U. Reyle, “N-PROLOG: an extension of Prolog with hypothetical implications,” Journal of Logic Programming 1 pp. 319–355 (1985).Google Scholar
  10. [10]
    H. Geffner and J. Pearl, “A distributed approach to diagnosis,” pp. 156–162 in Proc Third IEEE Conf. on AI Application, Orlando (1987).Google Scholar
  11. [11]
    J. Halpern and M. Rabin, “A Logic to Reason about Likelihood,” Artificial Intelligence 32 pp. 379–405 (1987).Google Scholar
  12. [12]
    S. Kripke, “A Completness Theorem in Modal Logic,” Journal of Symbolic Logic 24 pp. 1–14 (1959).Google Scholar
  13. [13]
    J. McCarthy, “Circumscription: a form of non-monotonic reasoning,” Artificial Intelligence, 13, pp. 27–39 (1980).Google Scholar
  14. [14]
    G. Molino, G. Cravetto, P. Torasso, and L. Console, “CHECK: a diagnostic expert system Combining HEuristic and Causal Knowledge,” Int. J. of Biomedical Measurement, Informatics and Control 1(4) pp. 182–193 (1986).Google Scholar
  15. [15]
    N.J. Nilsson, “Probabilistic Logic,” Artificial Intelligence 28 pp. 71–87 (1986).Google Scholar
  16. [16]
    R. Patil, “Causal representation of patient illness for electrolyte and acid-base diagnosis,” MIT/LCS/TR-267, Cambridge (1981).Google Scholar
  17. [17]
    J. Pearl, “On Evidential Reasoning in a Hierarchy of Hypotheses,” Artificial Intelligence 28 pp. 9–15 (1986).Google Scholar
  18. [18]
    J. Pearl, “Distributed Revision of Composite Beliefs,” Artificial Intelligence 33 pp. 173–215 (1987).Google Scholar
  19. [19]
    R. Reiter, “A Theory of Diagnosis from First Principles,” Artificial Intelligence 32 pp. 57–96 (1987).Google Scholar
  20. [20]
    N. Rescher, Hypothetical reasoning, North Holland (1964).Google Scholar
  21. [21]
    H. Ruspini, “Epistemic Logics, Probabilities and the Calculus of Evidence,” pp. 924–931 in Proc. IJCAI 87, Milano (1987).Google Scholar
  22. [22]
    L. Steels, “Second Generation Expert Systems,” Future Generation Computing Systems 1 pp. 213–221 (1985).Google Scholar
  23. [23]
    P. Szolovits and S.G. Pauker, “Categorical and probabilistic reasoning in medical diagnosis,” Artificial Intelligence 11 pp. 115–144 (1978).Google Scholar
  24. [24]
    P. Torasso and L. Console, “Causal Reasoning in Diagnostic Expert Systems,” pp. 598–605 in Proc. V Int. Conf. on Applications of Artificial Intelligence, Orlando (1987).Google Scholar
  25. [25]
    D.S. Warren, “Database updates in pure Prolog,” pp. 244–253 in Proc. Int. Conf. on Fifth Generation Computer Systems, Tokyo (1984).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Luca Console
    • 1
  • Pietro Torasso
    • 1
  1. 1.Dipartimento di InformaticaUniversita' di TorinoTorinoItaly

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