Guidelines for the verification and validation of expert system software and conventional software: Project summary. Volume 1

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This eight-volume report presents guidelines for performing verification and validation (V&V) on Artificial Intelligence (Al) systems with nuclear applications. The guidelines have much broader application than just expert systems; they are also applicable to object-oriented programming systems, rule-based systems, frame-based systems, model-based systems, neural nets, genetic algorithms, and conventional software systems. This is because many of the components of AI systems are implemented in conventional procedural programming languages, so there is no real distinction. The report examines the state of the art in verifying and validating expert systems. V&V methods traditionally applied to conventional software systems are evaluated for their ... continued below

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158 p.

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Mirsky, S.M.; Hayes, J.E. & Miller, L.A. March 1, 1995.

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Description

This eight-volume report presents guidelines for performing verification and validation (V&V) on Artificial Intelligence (Al) systems with nuclear applications. The guidelines have much broader application than just expert systems; they are also applicable to object-oriented programming systems, rule-based systems, frame-based systems, model-based systems, neural nets, genetic algorithms, and conventional software systems. This is because many of the components of AI systems are implemented in conventional procedural programming languages, so there is no real distinction. The report examines the state of the art in verifying and validating expert systems. V&V methods traditionally applied to conventional software systems are evaluated for their applicability to expert systems. One hundred fifty-three conventional techniques are identified and evaluated. These methods are found to be useful for at least some of the components of expert systems, frame-based systems, and object-oriented systems. A taxonomy of 52 defect types and their delectability by the 153 methods is presented. With specific regard to expert systems, conventional V&V methods were found to apply well to all the components of the expert system with the exception of the knowledge base. The knowledge base requires extension of the existing methods. Several innovative static verification and validation methods for expert systems have been identified and are described here, including a method for checking the knowledge base {open_quotes}semantics{close_quotes} and a method for generating validation scenarios. Evaluation of some of these methods was performed both analytically and experimentally. A V&V methodology for expert systems is presented based on three factors: (1) a system`s judged need for V&V (based in turn on its complexity and degree of required integrity); (2) the life-cycle phase; and (3) the system component being tested.

Physical Description

158 p.

Notes

INIS; OSTI as TI95010250

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  • Other Information: PBD: Mar 1995

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  • Other: TI95010250
  • Report No.: NUREG/CR--6316-Vol.1
  • Report No.: SAIC--95/1028-Vol.1
  • DOI: 10.2172/42512 | External Link
  • Office of Scientific & Technical Information Report Number: 42512
  • Archival Resource Key: ark:/67531/metadc688481

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  • March 1, 1995

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  • July 25, 2015, 2:20 a.m.

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  • Aug. 2, 2016, 2:38 p.m.

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Mirsky, S.M.; Hayes, J.E. & Miller, L.A. Guidelines for the verification and validation of expert system software and conventional software: Project summary. Volume 1, report, March 1, 1995; Washington D.C.. (digital.library.unt.edu/ark:/67531/metadc688481/: accessed September 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.