Expert Systems for Engineering Diagnosis: Styles, Requirements for Tools, and Adaptability
Abstract
Diagnosis seems a natural area for the application of expert systems. A diagnostic problem is often considered a challenge to engineers. They must exercise their intelligence to solve such a complicated puzzle. In solving a diagnostic problem, one must first observe and then try to locate possible failures by proper reasoning. The reasoning can be either empirical (by using accumulated experience) or functional (by using knowledge about system components and organization). Depending on the complexity of systems, their fault diagnosis can be quite complicated and time-consuming. Conventional methods do not seem suitable for sophisticated diagnostic problems. But expert systems can be quite effective in tackling a wide spectrum of diagnostic situations.
Keywords
Expert System Diagnostic System Inference Engine Hierarchical Organization Sound ChannelPreview
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References
- 1.B. G. Buchanan and E. H. Shortiffe (eds), Rule-Based Expert Systems: the MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, Reading, Mass. (1984).Google Scholar
- 2.A. van Melle, System Aids in Constructing Consultation Programs, UMI Research Press, Ann Arbor, MI (1981).Google Scholar
- 3.M. F. M. Tenorio and D. I. Moldovan, Mapping Production Systems into Multiprocessors, Proc. Int. Conf. Parallel Processing, pp. 56-62 (1985).Google Scholar
- 4.H. Erhig, Introduction of Algebraic Theory of Graph Grammars, LNCS Vol. 73, Springer-Verlag (1978).Google Scholar
- 5.B. C. Williams, Qualitative Analysis of MOS Circuits, Artificial Inteligence, 24, pp. 281–346 (1984).CrossRefGoogle Scholar
- 6.R. Davis, Diagnostic Reasoning Based on Structure and Behavior, Artificial Intelligence 24, pp. 347–410 (1984).CrossRefGoogle Scholar
- 7.R. Reiter, A Theory of Diagnosis From First Principle, Artificial Intelligence, 32, pp. 57–95 (1987).MathSciNetMATHCrossRefGoogle Scholar
- 8.J. de Kleer and B. C. Williams, Diagnosing Multiple Faults, Artificial Intelligence, 32, pp. 97–130 (1987).MATHCrossRefGoogle Scholar
- 9.Y. Peng and J. A. Reggia, Plausibility of Diagnostic Hypotheses: the Nature of Simplicity, Proc. AAAI-86, Philadelphia, P.A., pp. 140-145 (1986).Google Scholar
- 10.M. Abramovici and M. A. Breuer, Multiple Fault Diagnosis in Combinational Circuits based on An Effect-Cause Analysis, IEEE Trans. Comput. Vol. C-29, No. 6, pp. 451–460 (June 1980).MathSciNetCrossRefGoogle Scholar
- 11.D. C. Bossen and S. J. Hong, Cause-Effect Analysis for Multiple Fault Detection in Combinational Networks, IEEE Trans. Comput., Vol. C-20, No. 11, pp. 1252 (Nov. 1971).CrossRefGoogle Scholar
- 12.H. Pople, Heuristic methods for imposing structure on ill-structured problems: The structuring of medical diagnosis, In P. Szolovitz, ed., Artificial Intelligence in modicine. Boulder Colo: Westview Press, pp. 119–185 (1981).Google Scholar
- 13.J. A. Reggia, D. S. Nau and P. Y. Wang, Diagnostic Expert Systems based on A Set Covering Model, Int. Journal Man-Machine Studies, 19, pp. 437–460 (1983).CrossRefGoogle Scholar
- 14.T. Li and L. Y. Fang, Twoway diagnosis in traditional Chinese medicine, Proc. 3rd Internat. Conf. Applications of Artificial Intelligence, Orlando FL, pp. 937-945 (April 1986).Google Scholar
- 15.V. Dhar and H. E. Pople, Rule-based versus Structure-based Models for Explaining and Generating Expert Behavior, Commun. ACM, Vol. 30, No. 6, pp. 542–555 (June 1987).CrossRefGoogle Scholar
- 16.T. Li, “CODAR: A General Purpose Design Tool for Rule-based Engineering Expert Systems”, Journal of Artificial Intelligence in Engrg., (1987).Google Scholar
- 17.W. Burks, Peirce’s Theory of Adbuction, Philosophy of Science, 13, pp. 301–306 (1946).CrossRefGoogle Scholar
- 18.W. Burks, Chance, Cause, Reason, University of Chicago Press, Chicago, IL, (1977).MATHGoogle Scholar
- 19.E. Charniak and D. McDermott, Introduction to Artificial Intelligence, Chapter 8, Addison-Wesley, Reading, Mass. (1985).Google Scholar
- 20.J. A. Reggia, Abductive Inference, Proc. Expert Systems in Government Symp., McLean Virginia: IEEE Computer Soci. Press, pp. 484-487 (1985).Google Scholar
- 21.A. Goldberg and I. Pohl, Is Complexity Theory of Use to AI?, Artificial and Human intelligence (edited review papers of Internat. NATO Symp. Artificial and Human Intelligence Lyon France), edited by A. Elithorn and R. Banerji, North-Holland, pp. 43-56 (1984).Google Scholar