| Generic expert system shell for diagnostic reasoning |
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International conference on Industrial and engineering applications of artificial intelligence and expert systems
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Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
table of contents
Tullahoma, Tennessee, United States
Pages: 7 - 12
Year of Publication: 1988
ISBN:0-89791-271-3
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Downloads (6 Weeks): 2, Downloads (12 Months): 38, Citation Count: 3
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ABSTRACT
Rule based expert systems provide a modular and uniform approach to representing knowledge, however it has been recognized that rule-based systems become increasingly difficult to understand and maintain as the number of rules grow. Expert systems today are developed on general purpose inference shells that offer general purpose paradigms which do not take into considerations the type of problems being solved. It is up to the users to create the meta level control to prevent rule interference, and for the rules to function properly. This task tends to become increasingly difficult in direct proportion to the size of the accumulated knowledge.
The solution is in a new generation of Application Specific Expert System Tools that are designed with specific paradigms and knowledge representation methodology that meet the requirements of a specific domain. This concept is examplified in the work presented here that introduces a generic expert systems shell for diagnostic reasoning. Domain knowledge is represented as five different classes of objects. A paradigm for diagnostic reasoning is built into the inference algorithm to become part of the inference shell, replacing the usual general purpose forward or backward chaining algorithm.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Ben88
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Ben-Bassat, Moshe, et al, "AI-TEST A Real Life Expert System for Electronic Troubleshooting", Proceedings of The Fourth Conference on Artificial Intelligence Applications, March 1988.
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Dav84
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Davis, Randall and Jonathan King, "The Origin of Rule-Based Systems in AI", in Rule-Based Expert Systems, Addison- Wesley, 1984.
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Dav84a
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Davis, Randall and Bruce Buchanan, "Meta- Level Knowledge", in Rule-Based Expert Systems, Addison-Wesley, 1984.
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Bru86
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van de Brug, Arnold; Judith Bachant, and John Mcdermott, "The Taming of RI", IEEE Ai Expert, Fall 1986.
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Cha86
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Chandrasekaran, B. "Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design", IEEE AI Expert, Fall 1986.
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Fin85
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Fink, K. Pamela, John C. Lusth, and Joe W. Duran, "A General Expert System Design for Diagnostic Problem Solving", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, No. 5, September 1985.
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Gom81
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Gomez, F. and B. Chandrasekaran, "Knowledge Organization and distribution for Medical Diagnosis", IEEE Trans. Systems, Man and Cybernetics, Vol.ll, No. 1, Jan. 1981.
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Lim88
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CITED BY 3
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Suhayya Abu-Hakima , Philippe Davidson , Mike Halasz , Sieu Phan, Jet engine technical advisor (JETA), Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems, p.154-160, June 1989, Tullahoma, Tennessee, United States
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Philippe L. Davidson , Mike Halasz , Sieu Phan , Suhayya Abu Hakima, Intelligent troubleshooting of complex machinery, Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems, p.16-22, June 1990, Charleston, South Carolina, United States
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