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Generic expert system shell for diagnostic reasoning
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Source International conference on Industrial and engineering applications of artificial intelligence and expert systems archive
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
Author
Wei-Han Chu  Varian Associates, Palo Alto, CA
Sponsor
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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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.

 
Ben88
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.
 
Dav84
Davis, Randall and Jonathan King, "The Origin of Rule-Based Systems in AI", in Rule-Based Expert Systems, Addison- Wesley, 1984.
 
Dav84a
Davis, Randall and Bruce Buchanan, "Meta- Level Knowledge", in Rule-Based Expert Systems, Addison-Wesley, 1984.
 
Bru86
van de Brug, Arnold; Judith Bachant, and John Mcdermott, "The Taming of RI", IEEE Ai Expert, Fall 1986.
 
Cha86
Chandrasekaran, B. "Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design", IEEE AI Expert, Fall 1986.
 
Fin85
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.
 
Gom81
Gomez, F. and B. Chandrasekaran, "Knowledge Organization and distribution for Medical Diagnosis", IEEE Trans. Systems, Man and Cybernetics, Vol.ll, No. 1, Jan. 1981.
Lim88



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