File:Automated pattern recognition- self-generating expert systems for the future (IA jresv90n6p521 A1b).pdf
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Summary[edit]
Automated pattern recognition: self-generating expert systems for the future ( ) | ||
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Author |
Isenhour, T.L. |
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Title |
Automated pattern recognition: self-generating expert systems for the future |
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Volume | 90 | |
Publisher |
National Bureau of Standards |
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Description |
Journal of Research of the National Bureau of Standards Subjects: artificial intelligence; chemical analysis; expert systems; pattern recognition; relational data bases; robotics. |
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Language | English | |
Publication date |
1985 publication_date QS:P577,+1985-00-00T00:00:00Z/9 |
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Current location |
IA Collections: NISTJournalofResearch; NISTresearchlibrary; fedlink |
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Accession number |
jresv90n6p521_A1b |
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Source | ||
Permission (Reusing this file) |
The Journal of Research of the National Institute of Standards and Technology is a publication of the U.S. Government. The papers are in the public domain and are not subject to copyright in the United States. However, please pay special attention to the individual works to make sure there are no copyright restrictions indicated. Individual works may require securing other permissions from the original copyright holder. |
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This file has been identified as being free of known restrictions under copyright law, including all related and neighboring rights. |
https://creativecommons.org/publicdomain/mark/1.0/PDMCreative Commons Public Domain Mark 1.0falsefalse
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Short title | Automated pattern recognition: self-generating expert systems for the future |
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Image title | Chemometrics and pattern recognition had their start in chemistry in the late 1960's. The most recent review of the area by Michael DeLaney listed 438 journal articles and books. The three most important areas of future development will be Expert Systems, Relational Data Bases, and Robotics. It should now be possible to combine existing robotics and artificial intelligence software to create a system which will generate its own expert systems using relational data bases. The data will be in the chemical domain and the system I describe we are calling the Analytical Director. The Analytical Director will be an artificial intelligence/robotic expert system for the analytical laboratory. The Analytical Director will develop, test, implement and interpret chemical analysis procedures. It will learn from its own experience, the experience of others and communicate what it has learned to others. The Analytical Director will be a self-generating Expert System. I believe that such systems will, in the future, provide all the advantages of pattern recognition, expert systems and relational data bases in experimental settings. Problems will continue to be defined by human beings, but more and more, the laboratory will design, execute and evaluate its own experiments. |
Author | Isenhour |
Keywords |
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Software used | [[w:|]] |
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Encrypted | no |
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Version of PDF format | 1.6 |