Abstract
In this paper we describe the main characteristic of EXODUS: a distributed diagnostic expert system architecture. Knowledge in EXODUS is represented by means of a connectionist-like network and no centralized inference engine has been defined. Control is distributed among the nodes and evidence combination and propagation play a fundamental role in the reasoning process. Such an architecture is proposed as a mechanism for reaching very fastly a set of hypothesis accounting for the data characterizing a diagnostic problem (mimicing the ability of human experts who can provide initial solutions to a problem very easily). In the paper we describe in detail the architecture of the system and the mechanisms we introduced for dealing with uncertainty. In the final part of the paper we suggest that such a form of associational reasoning should be integrated with some form of deep reasoning (able to provide detailed explanations and to solve complex cases).
The research described in this paper has been partially supported by CNR and MPI. The authors are grateful to A.F. Rocha (Univ. Campinas - Brasil) for many helpful discussions and suggestions.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Ahuja, S.B., Soh, W-Y, and Schwartz, A., “A Connectionist Processing Metaphor for Diagnostic Reasoning,” International Journal of Intelligent Systems 4(2) pp. 155–180 (1989).
Console, L., Theseider Dupre', D., and Torasso, P., “A Theory of Diagnosis for Incomplete Causal Models,” pp. 1311–1317 in Proc. 11th IJCAI, Detroit (1989).
Console, L. and Torasso, P., “Hypothetical Reasoning in Causal Models,” International Journal of Intelligent Systems 5(1) pp. 83–124 (1990).
Feldman, J. and Ballard, D., “Connectionist Models and their properties,” Cognitive Science 6 pp. 205–254 (1982).
Gallant, S., “Connectionist expert systems,” Communications of the ACM 31(2) pp. 152–169 (1988).
Gutknecht, M. and Pfeiffer, R., “Experiments with a Hybrid Architecture: Integrating Expert Systems with Connectionist Networks,” pp. 287–299 in Proc. 10th Int. Work. on Expert Systems and Their Applications (Conf. on 2nd Generation Expert Systems), Avignon (1990).
Hart, E. and Wyatt, J., “Connectionist Models in Medicine: An Investigation of their Potential,” pp. 115–124 in Lecture Notes in Medical Informatics 38, Springer Verlag (1989).
Leao, B. and Rocha, A.F., “A Methodology proposed for Knowledge Acquisition,” pp. 1042–1049 in Proc. Int. Congress Medical Informatics Europe 87, Rome (1987).
Lesmo, L., Magnani, D., and Torasso, P., “A Deterministic Analyser for the Interpretation of Natural Language Commands,” pp. 440–442 in Proc. 7th IJCAI, Vancouver (1981).
Marcus, M., A Theory of Syntactic Recognition for Natural Language, MIT Press (1980).
McClelland, J. and Rumelhart, D., Parallel Distributed Processing, Vol. 2, MIT Press (1986).
Peng, Y. and Reggia, J., “A connectionist model for diagnostic problem solving,” IEEE Trans. on Systems, Man and Cybernetics SMC-19(2) pp. 285–298 (1989).
Reggia, J.A., Nau, D.S., and Wang, P.Y., “Diagnostic expert systems based on a set covering model,” Int. J. of Man-Machine Studies 19 pp. 437–460 (1983).
Rollinger, C.H., “How to represent evidence — Aspects of Uncertain Reasoning,” pp. 358–361 in Proc. IJCAI 83,, Karlsruhe (1983).
Shastri, L., “A connectionist approach to knowledge representation and limited inference,” Cognitive Science 12 pp. 331–392 (1988).
Steels, L., “Components of Expertise,” AI Magazine 11(2) pp. 30–49 (1990).
Torasso, P. and Console, L., Diagnostic Problem Solving: Combining Heuristic, Approximate and Causal Reasoning, Van Nostrand Reinhold (1989).
Touretzky, D.S. and Hinton, G., “Symbols among the neurons,” pp. 238–243 in Proc. 9th IJCAI, Los Angeles (1985).
Zadeh, L.A., “Fuzzy Sets as a Basis for a Theory of Possibility,” Fuzzy Sets and Systems 1 pp. 3–28 (1978).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Console, L., Borlo, C., Casale, A., Torasso, P. (1991). Dealing with uncertainty in a distributed expert system architecture. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028144
Download citation
DOI: https://doi.org/10.1007/BFb0028144
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54346-6
Online ISBN: 978-3-540-47580-4
eBook Packages: Springer Book Archive