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Proceedings Paper

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation
Author(s): Joseph L. Nieten; Roger Burke
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Paper Abstract

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

Paper Details

Date Published: 23 March 1993
PDF: 8 pages
Proc. SPIE 1963, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, (23 March 1993); doi: 10.1117/12.141752
Show Author Affiliations
Joseph L. Nieten, GHG Corp. (United States)
Roger Burke, NASA Johnson Space Ctr. (United States)


Published in SPIE Proceedings Vol. 1963:
Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry
Usama M. Fayyad; Ramasamy Uthurusamy, Editor(s)

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