Abstract
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.
The research described in this paper has been partially supported by CNR and MPI
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
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).
D.G. Bobrow (ed), “Special issue on qualitative reasoning,” Artificial Intelligence 24(1984).
B. Chandrasekaran and R. Milne (eds), “Special Section on Reasoning about Structure, Behavior and Function,” Sigart Newsletter 93 pp. 4–55 (1985).
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).
L. Console and P. Torasso, “Hypothetical Reasoning in Causal Models,” Techn. Rep. Univ. Torino (May 1987).
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).
J. deKleer, “An Assumption-based TMS,” Artificial Intelligence 28 pp. 127–162 (1986).
J. deKleer and B.C. Williams, “Diagnosing Multiple Faults,” Artificial Intelligence 32 pp. 97–130 (1987).
D.M. Gabbay and U. Reyle, “N-PROLOG: an extension of Prolog with hypothetical implications,” Journal of Logic Programming 1 pp. 319–355 (1985).
H. Geffner and J. Pearl, “A distributed approach to diagnosis,” pp. 156–162 in Proc Third IEEE Conf. on AI Application, Orlando (1987).
J. Halpern and M. Rabin, “A Logic to Reason about Likelihood,” Artificial Intelligence 32 pp. 379–405 (1987).
S. Kripke, “A Completness Theorem in Modal Logic,” Journal of Symbolic Logic 24 pp. 1–14 (1959).
J. McCarthy, “Circumscription: a form of non-monotonic reasoning,” Artificial Intelligence, 13, pp. 27–39 (1980).
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).
N.J. Nilsson, “Probabilistic Logic,” Artificial Intelligence 28 pp. 71–87 (1986).
R. Patil, “Causal representation of patient illness for electrolyte and acid-base diagnosis,” MIT/LCS/TR-267, Cambridge (1981).
J. Pearl, “On Evidential Reasoning in a Hierarchy of Hypotheses,” Artificial Intelligence 28 pp. 9–15 (1986).
J. Pearl, “Distributed Revision of Composite Beliefs,” Artificial Intelligence 33 pp. 173–215 (1987).
R. Reiter, “A Theory of Diagnosis from First Principles,” Artificial Intelligence 32 pp. 57–96 (1987).
N. Rescher, Hypothetical reasoning, North Holland (1964).
H. Ruspini, “Epistemic Logics, Probabilities and the Calculus of Evidence,” pp. 924–931 in Proc. IJCAI 87, Milano (1987).
L. Steels, “Second Generation Expert Systems,” Future Generation Computing Systems 1 pp. 213–221 (1985).
P. Szolovits and S.G. Pauker, “Categorical and probabilistic reasoning in medical diagnosis,” Artificial Intelligence 11 pp. 115–144 (1978).
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).
D.S. Warren, “Database updates in pure Prolog,” pp. 244–253 in Proc. Int. Conf. on Fifth Generation Computer Systems, Tokyo (1984).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1988 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Console, L., Torasso, P. (1988). A logical approach to deal with incomplete causal models in diagnostic problem solving. In: Bouchon, B., Saitta, L., Yager, R.R. (eds) Uncertainty and Intelligent Systems. IPMU 1988. Lecture Notes in Computer Science, vol 313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-19402-9_80
Download citation
DOI: https://doi.org/10.1007/3-540-19402-9_80
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-19402-6
Online ISBN: 978-3-540-39255-2
eBook Packages: Springer Book Archive