EDNA: expert digraph network analysis

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EDNA: expert digraph network analysis

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International conference on Industrial and engineering applications of artificial intelligence and expert systems archive
Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1 table of contents

Charleston, South Carolina, United States

Pages: 70 - 75

Year of Publication: 1990

ISBN:0-89791-372-8

Author

V. V. Dixit

Computer Aided Engineering, Engineering Applications, Rockwell International, Mail Stop AB-36, 12214 Lakewood Boulevard, Downey, CA

Sponsor

SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

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ABSTRACT

Fault models are usually represented by trees or digraphs. Tree representations have fast algorithms. Digraph representations can have loops and they are easy to build and maintain. Neither of the two representations incorporate heuristic knowledge. Here we propose an AI based method to blend the representational capabilities of digraphs and the analytical capabilities of trees. The fast tree algorithms combined with digraph representation would make this tool valuable in the design process as well as in the capture of final design knowledge.

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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	VGRH	W.E. Vesley, F.F. Goldberg, N.H. Roberts, and D.F. Haasi. Fault Tree Handbook. Systems and Reliability Research Office of Nuclear Regulatory Research. US Nuclear Regulatory Commission Washington D.C. 20555.