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Representing diagnosis knowledge

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Abstract

This paper considers therepresentation problem: namely how to go from an abstract problem to a formal representation of the problem. We consider this for two conceptions of logic-based diagnosis, namely abductive and consistency-based diagnosis. We show how to represent diagnostic problems that can be conceptualised causally in each of the frameworks, and show that both representations of the same problems give the same answers. This is a local transformation that allows for an expressive (albeit propositional) language for giving the constraints on what symptoms and causes can coexist, including non-strict causation. This non-strict causation can be represented in each frameworkwithout adding special reasoning constructs to either framework. This is presented as a starting point for a study of the representation problem in diagnosis, rather than as an end in itself.

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References

  1. K. L. Clark. Negation as failure, in:Logic and Databases, eds. H. Gallaire and J. Minker (Plenum Press, New York, 1978) pp. 293–322.

    Google Scholar 

  2. L. Console, D. Theseider Dupré and P. Torasso, Abductive reasoning through direct deduction from completed domai[225zn models, in:Methodologies for Intelligent Systems 4, ed. W. R. Zbigniew (Elsiever, Amsterdam, 1989) pp. 175–182.

    Google Scholar 

  3. L. Console, D. Theseider Dupré and P. Torasso, On the relationship between abduction and deduction, J. Logic and Comput. 1(5):661–690, 1991.

    Google Scholar 

  4. P. T. Cox and T. Pietrzykowski, General diagnosis by abductive inference, Technical Report CS8701, Computer Science, Technical University of Nove Scotia, Halifax (April 1987).

    Google Scholar 

  5. E. Davis,Representations of Commonsense Knowledge (Morgan Kaufmann, San Mateo, CA, 1990).

    Google Scholar 

  6. J. de Kleer and B. C. Williams, Diagnosing multiple faults, Artificial Intelligence 32(1) (1987) 97–130.

    Google Scholar 

  7. M. R. Genesereth, The use of design descriptions in automated diagnosis, Artificial Intelligence 24(1–3) (1984) 411–436.

    Google Scholar 

  8. M. L. Ginsberg (ed.),Readings in Nonmonotonic Reasoning (Morgan Kaufmann, Los Altos, CA, 1987).

    Google Scholar 

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

    Google Scholar 

  10. D. Lin, A probabilistic network of predicates,Proc. 8th Conf. on Uncertainty in Artificial Intelligence, eds. D. Dubois, M. P. Wellman, B. D'Ambrosio and P. Smets, Stanford University, July 1992, pp. 174–181.

  11. J. W. Lloyd,Foundations of Logic Programming, Symbolic Computation Series, 2nd Ed. (Springer-Verlag, Berlin, 1987).

    Google Scholar 

  12. J. McCarthy and P. J. Hayes, Some philosophical problems from the standpoint of artificial intelligence, in:Machine Intelligence 4, eds. M. Meltzer and D. Michie (Edinburgh University Press, 1969) pp. 463–502.

  13. J. Pearl, Embracing causation in default reasoning, Artificial Intelligence 35(2) (1988) 259–271.

    Google Scholar 

  14. D. Poole, Representing knowledge for logic-based diagnosis,Int. Conf. on Fifth Generation Computing Systems, Tokyo, Japan, November 1988, pp. 1282–1290.

  15. D. Poole, Explanation and prediction: an architecture for default and abductive reasoning, Computational Intelligence 5(2) (1989) 97–110.

    Google Scholar 

  16. D. Poole, Normality and faults in logic-based diagnosis,Proc. 11th Int. Joint Conf. on Artificial Intelligence, Detroit, August 1989, pp. 1304–1310.

  17. D. Poole, A methodology for using a default and abductive reasoning system, Int. J. Intelligent Syst. 5(5) (1990) 521–548.

    Google Scholar 

  18. D. Poole, Probabilistic Horn abduction and Bayesian networks, Artificial Intelligence 64(1) (1993) 81–129.

    Google Scholar 

  19. D. Poole, R. Goebel and R. Aleliunas, Theorist: A logical reasoning system for defaults and diagnosis, in:The Knowledge Frontier: Essays in the Representation of Knowledge, eds. N. Cereone and G. McCalla (Springer-Verlag, New York, NY, 1987) pp. 331–352.

    Google Scholar 

  20. D. Poole and G. Provan, What is the most likely diagnosis? in:Uncertainty in Artificial Intelligence 6, eds. P. P. Bonissone, M. Henrion, L. N. Kanal and J. F. Lemmer (Elsevier, Amsterdam, 1991) pp. 89–105.

    Google Scholar 

  21. H. E. Pople, Jr., On the mechanization of abductive logic,Proc. 3rd Int. Joint Conf. on Artificial Intelligence, Stanford, August 1973, pp. 147–152.

  22. J. Reggia, D. Nau and P. Wang, A formal model of diagnostic inference, Inf. Sci. (1985) 227–285.

  23. R. Reiter, A theory of diagnosis from first principles, Artificial Intelligence 32(1) (1987) 57–95.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of British Columbia, V6T 1Z4, Vancouver, B.C., Canada

    David Poole

  2. Canadian Institute for Advanced Research, Canada

    David Poole

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  1. David Poole
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Poole, D. Representing diagnosis knowledge. Ann Math Artif Intell 11, 33–50 (1994). https://doi.org/10.1007/BF01530736

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