Verification and Validation of Rule-Based Expert Systems

Verification and Validation of Rule-Based Expert Systems

Author: Suzanne Smith

Publisher: CRC Press

Published: 2018-10-08

Total Pages: 224

ISBN-13: 149871935X

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This book presents an innovative approach to verifying and validating rule-based expert systems. It features a complete set of techniques and tools that provide a more formal, objective, and automated means of carrying out verification and validation procedures. Many of the concepts behind these procedures have been adapted from conventional software, while others have required that new techniques or tools be created because of the uniqueness of rule-based expert systems. Verification and Validation of Rule-Based Expert Systems is a valuable reference for electrical engineers, software engineers, artificial intelligence experts, and computer scientists involved with object-oriented development, expert systems, and programming languages.


Guidelines for the Verification and Validation of Expert System Software and Conventional Software

Guidelines for the Verification and Validation of Expert System Software and Conventional Software

Author:

Publisher:

Published: 1995

Total Pages: 176

ISBN-13:

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By means of a literature survey, a comprehensive set of methods was identified for the verification and validation of conventional software. The 153 methods so identified were classified according to their appropriateness for various phases of a developmental life-cycle -- requirements, design, and implementation; the last category was subdivided into two, static testing and dynamic testing methods. The methods were then characterized in terms of eight rating factors, four concerning ease-of-use of the methods and four concerning the methods' power to detect defects. Based on these factors, two measurements were developed to permit quantitative comparisons among methods, a Cost-Benefit metric and an Effectiveness Metric. The Effectiveness Metric was further refined to provide three different estimates for each method, depending on three classes of needed stringency of V & V (determined by ratings of a system's complexity and required-integrity). Methods were then rank-ordered for each of the three classes by terms of their overall cost-benefits and effectiveness. The applicability was then assessed of each for the identified components of knowledge-based and expert systems, as well as the system as a whole.


Reference Information for the Software Verification and Validation Process

Reference Information for the Software Verification and Validation Process

Author: Dolores R. Wallace

Publisher: DIANE Publishing

Published: 1996

Total Pages: 97

ISBN-13: 0788143409

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Computing systems are employed in the health care environment in efforts to increase reliability of care and reduce costs. Software verification and validation (V&V) is an aid in determining that the software requirements are implemented correctly and completely and are traceable to system requirements. It helps to ensure that those system functions controlled by software are secure, reliable, and maintainable. Software V&V is conducted throughout the planning, development and maintenance of software systems, including knowledge based systems, and may assist in assuring appropriate reuse of software.


Guidelines for the Verification and Validation of Expert System Software and Conventional Software

Guidelines for the Verification and Validation of Expert System Software and Conventional Software

Author:

Publisher:

Published: 1995

Total Pages: 122

ISBN-13:

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This report presents the results of the Knowledge Base Certification activity of the expert systems verification and validation (V & V) guideline development project which is jointly funded by the US Nuclear Regulatory Commission and the Electric Power Research Institute. The ultimate objective is the formulation of guidelines for the V & V of expert systems for use in nuclear power applications. This activity is concerned with the development and testing of various methods for assuring the quality of knowledge bases. The testing procedure used was that of behavioral experiment, the first known such evaluation of any type of V & V activity. The value of such experimentation is its capability to provide empirical evidence for -- or against -- the effectiveness of plausible methods in helping people find problems in knowledge bases. The three-day experiment included 20 participants from three nuclear utilities, the Nuclear Regulatory Commission's Technical training Center, the University of Maryland, EG & G Idaho, and SAIC. The study used two real nuclear expert systems: a boiling water reactor emergency operating procedures tracking system and a pressurized water reactor safety assessment systems. Ten participants were assigned to each of the expert systems. All participants were trained in and then used a sequence of four different V & V methods selected as being the best and most appropriate for study on the basis of prior evaluation activities. These methods either involved the analysis and tracing of requirements to elements in the knowledge base (requirements grouping and requirements tracing) or else involved direct inspection of the knowledge base for various kinds of errors. Half of the subjects within each system group used the best manual variant of the V & V methods (the control group), while the other half were supported by the results of applying real or simulated automated tools to the knowledge bases (the experimental group).


Guidelines for the Verification and Validation of Expert System Software and Conventional Software

Guidelines for the Verification and Validation of Expert System Software and Conventional Software

Author:

Publisher:

Published: 1995

Total Pages: 158

ISBN-13:

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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 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.