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

Abstract

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 beenmore » 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.« less

Authors:
; ;  [1]
  1. Science Applications International Corp., McLean, VA (United States)
Publication Date:
Research Org.:
Nuclear Regulatory Commission, Washington, DC (United States). Div. of Systems Technology; Electric Power Research Inst., Palo Alto, CA (United States). Nuclear Power Div.; Science Applications International Corp., McLean, VA (United States)
Sponsoring Org.:
Nuclear Regulatory Commission, Washington, DC (United States); Electric Power Research Inst., Palo Alto, CA (United States)
OSTI Identifier:
42512
Report Number(s):
NUREG/CR-6316-Vol.1; SAIC-95/1028-Vol.1
ON: TI95010250; TRN: 95:011048
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: Mar 1995
Country of Publication:
United States
Language:
English
Subject:
22 NUCLEAR REACTOR TECHNOLOGY; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; NUCLEAR POWER PLANTS; EXPERT SYSTEMS; COMPUTERIZED CONTROL SYSTEMS; VALIDATION; VERIFICATION; NUCLEAR INDUSTRY; ARTIFICIAL INTELLIGENCE

Citation Formats

Mirsky, S.M., Hayes, J.E., and Miller, L.A.. Guidelines for the verification and validation of expert system software and conventional software: Project summary. Volume 1. United States: N. p., 1995. Web. doi:10.2172/42512.
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. United States. doi:10.2172/42512.
Mirsky, S.M., Hayes, J.E., and Miller, L.A.. Wed . "Guidelines for the verification and validation of expert system software and conventional software: Project summary. Volume 1". United States. doi:10.2172/42512. https://www.osti.gov/servlets/purl/42512.
@article{osti_42512,
title = {Guidelines for the verification and validation of expert system software and conventional software: Project summary. Volume 1},
author = {Mirsky, S.M. and Hayes, J.E. and Miller, L.A.},
abstractNote = {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.},
doi = {10.2172/42512},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 1995},
month = {Wed Mar 01 00:00:00 EST 1995}
}

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