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This study is part of the preparatory work needed for establishing a foundational comprehension of specific aspects related to the research goal, which is to investigate the multidimensional interplay of individual factors and context in shaping trust towards Artificial Intelligence Expert Systems. This study is primarily a validation study in a representative German sample. It aims to validate a generally satisfying description of how an AIES should be described such that people can make an informed decision on whether they would want to use that system or not. Initially, a set of descriptive elements was introduced and exploratively analyzed in a predecessor study (https://doi.org/10.17605/OSF.IO/YQZGK). The descriptive elements identified as 'most essential' components of a comprehensive yet concise description are presented herein. It further aims to replicate and confirm the factor structure of a translated propensity to trust scale by Frazier et al. 2013. Additionally, it identifies people's risk and trust perceptions in five different contexts (medicine, psychotherapy, law, education, and finance).

The original objectives of the study consisted of five broad areas of investigation: criteria and issues for explanation of communication and tracking system anomaly detection, isolation, and recovery; data storage simplification issues for fault detection expert systems; data selection procedures for decision tree pruning and optimization to enhance the abstraction of pertinent information for clear explanation; criteria for establishing levels of explanation suited to needs; and analysis of expert system interaction and modularization. Progress was made in all areas, but to a lesser extent in the criteria for establishing levels of explanation suited to needs. Among the types of expert systems studied were those related to anomaly or fault detection, isolation, and recovery.

Turbo Prolog is a recently available, compiled version of the programming language Prolog. Turbo Prolog is designed to provide not only a Prolog compiler, but also a program development environment for the IBM Personal Computer family. An evaluation of Turbo Prolog was made, comparing its features to other versions of Prolog and to the community of languages commonly used in artificial intelligence (AI) research and development. Three programs were employed to determine the execution speed of Turbo Prolog applied to various problems. The results of this evaluation demonstrated that Turbo Prolog can perform much better than many commonly employed AI languages for numerically intensive problems and can equal the speed of development languages such as OPS5+ and CLIPS, running on the IBM PC. Applications for which Turbo Prolog is best suited include those which (1) lend themselves naturally to backward-chaining approaches, (2) require extensive use of mathematics, (3) contain few rules, (4) seek to make use of the window/color graphics capabilities of the IBM PC, and (5) require linkage to programs in other languages to form a complete executable image.

Turbo Prolog is a recently available, compiled version of the programming language Prolog. Turbo Prolog is designed to provide not only a Prolog compiler, but also a program development environment for the IBM Personal Computer family. An evaluation of Turbo Prolog was made, comparing its features to other versions of Prolog and to the community of languages commonly used in artificial intelligence (AI) research and development. Three programs were employed to determine the execution speed of Turbo Prolog applied to various problems. The results of this evaluation demonstrated that Turbo Prolog can perform much better than many commonly employed AI languages for numerically intensive problems and can equal the speed of development languages such as OPS5+ and CLIPS, running on the IBM PC. Applications for which Turbo Prolog is best suited include those which (1) lend themselves naturally to backward-chaining approaches, (2) require extensive use of mathematics, (3) contain few rules, (4) seek to make use of the window/color graphics capabilities of the IBM PC, and (5) require linkage to programs in other languages to form a complete executable image.

Turbo Prolog is a recently available, compiled version of the programming language Prolog. Turbo Prolog is designed to provide not only a Prolog compiler, but also a program development environment for the IBM Personal Computer family. An evaluation of Turbo Prolog was made, comparing its features to other versions of Prolog and to the community of languages commonly used in artificial intelligence (AI) research and development. Three programs were employed to determine the execution speed of Turbo Prolog applied to various problems. The results of this evaluation demonstrated that Turbo Prolog can perform much better than many commonly employed AI languages for numerically intensive problems and can equal the speed of development languages such as OPS5+ and CLIPS, running on the IBM PC. Applications for which Turbo Prolog is best suited include those which (1) lend themselves naturally to backward-chaining approaches, (2) require extensive use of mathematics, (3) contain few rules, (4) seek to make use of the window/color graphics capabilities of the IBM PC, and (5) require linkage to programs in other languages to form a complete executable image.

Turbo Prolog is a recently available, compiled version of the programming language Prolog. Turbo Prolog is designed to provide not only a Prolog compiler, but also a program development environment for the IBM Personal Computer family. An evaluation of Turbo Prolog was made, comparing its features to other versions of Prolog and to the community of languages commonly used in artificial intelligence (AI) research and development. Three programs were employed to determine the execution speed of Turbo Prolog applied to various problems. The results of this evaluation demonstrated that Turbo Prolog can perform much better than many commonly employed AI languages for numerically intensive problems and can equal the speed of development languages such as OPS5+ and CLIPS, running on the IBM PC. Applications for which Turbo Prolog is best suited include those which (1) lend themselves naturally to backward-chaining approaches, (2) require extensive use of mathematics, (3) contain few rules, (4) seek to make use of the window/color graphics capabilities of the IBM PC, and (5) require linkage to programs in other languages to form a complete executable image.

Includes bibliographical references (p. [343]-383) and index

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xxi, 367p. : 23 cm

xxi, 367p. : 23 cm

A number of Artificial Intelligence (AI) systems have appeared in recent years whose approaches appear very fruitful for a wide variety of real- world tasks that are performed by knowledgeable (but not necessarily understanding) experts. In the interest of acquainting a wider audience to a representative sampling of these systems, and especially to the approaches they embody, the following selective reading list is proposed. These readings are not overly technical, and usually make their main points by way of examples of natural language dialog between a user and the expert AI system. Papers usually contain further references to related work and background material. The intended audience includes engineers (especially designers), social scientists, computer specialists without AI background, and other professionals. In the following, I have grouped papers roughly into categories, some of which contain specific systems while others are broader overviews or even general introductions to the wider field of AI. In the interest of being representative, a number of systems of approximately equal significance to those given hear have been omitted. The papers here do in fact refer to these others.

The study of knowledge acquisition should be administered in two parts. CERL: This part will concentrate on actual systems such as CRANES and BID/NO BID. We shall report on our practical experience both in dealing with the domain experts and in coding the acquired knowledge. SERC; This part will draw more on other people's experience and will be based largely on the profiling of situations in which expert systems have been created and used.

The study of knowledge acquisition should be administered in two parts. CERL: This part will concentrate on actual systems such as CRANES and BID/NO BID. We shall report on our practical experience both in dealing with the domain experts and in coding the acquired knowledge. SERC; This part will draw more on other people's experience and will be based largely on the profiling of situations in which expert systems have been created and used.

The study of knowledge acquisition should be administered in two parts. CERL: This part will concentrate on actual systems such as CRANES and BID/NO BID. We shall report on our practical experience both in dealing with the domain experts and in coding the acquired knowledge. SERC; This part will draw more on other people's experience and will be based largely on the profiling of situations in which expert systems have been created and used.

The study of knowledge acquisition should be administered in two parts. CERL: This part will concentrate on actual systems such as CRANES and BID/NO BID. We shall report on our practical experience both in dealing with the domain experts and in coding the acquired knowledge. SERC; This part will draw more on other people's experience and will be based largely on the profiling of situations in which expert systems have been created and used.

The study of knowledge acquisition should be administered in two parts. CERL: This part will concentrate on actual systems such as CRANES and BID/NO BID. We shall report on our practical experience both in dealing with the domain experts and in coding the acquired knowledge. SERC; This part will draw more on other people's experience and will be based largely on the profiling of situations in which expert systems have been created and used.

The study of knowledge acquisition should be administered in two parts. CERL: This part will concentrate on actual systems such as CRANES and BID/NO BID. We shall report on our practical experience both in dealing with the domain experts and in coding the acquired knowledge. SERC; This part will draw more on other people's experience and will be based largely on the profiling of situations in which expert systems have been created and used.

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a straightforward task. This paper presents a statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. This paper describes the systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data. The development method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 algorithm. (rrh)

The growing cost of operating and maintaining its central heating plants (CHPs) has forced the Army to seek alternatives to traditional methods of running these facilities. Computer technology offers the potential to automate and assist in many tasks, such as in the diagnosis of equipment malfunctions and failures in Army CHPs. An automated diagnostic tool for heating plant equipment could lower the cost of human labor by freeing personnel for higher priority work. Automatic diagnosis of problems could also reduce downtime for repair, promote thermal efficiency, and improve on-line reliability. Researchers at the U.S. Army Construction Engineering Research Laboratories (USACERL) investigated the application of artificial intelligence (AI) using knowledge-based expert systems to the monitoring and diagnosing of CHP boiler operations. A prototype system (MAD) was developed to Monitor And Diagnose boiler failure or identify inefficient operation, and recommend action to optimize combustion efficiency. The system includes a knowledge base containing rules for diagnosing the condition of major package boiler components. Minimum system requirements for MAD are an IBM-compatible AT-class personal computer (PC) with 640K base memory and 1 megabyte extended memory, 1.5 megabytes of free hard drive space, a color graphics adaptor (CGA), and DOS 3.0 (or higher).

The growing cost of operating and maintaining its central heating plants (CHPs) has forced the Army to seek alternatives to traditional methods of running these facilities. Computer technology offers the potential to automate and assist in many tasks, such as in the diagnosis of equipment malfunctions and failures in Army CHPs. An automated diagnostic tool for heating plant equipment could lower the cost of human labor by freeing personnel for higher priority work. Automatic diagnosis of problems could also reduce downtime for repair, promote thermal efficiency, and improve on-line reliability. Researchers at the U.S. Army Construction Engineering Research Laboratories (USACERL) investigated the application of artificial intelligence (AI) using knowledge-based expert systems to the monitoring and diagnosing of CHP boiler operations. A prototype system (MAD) was developed to Monitor And Diagnose boiler failure or identify inefficient operation, and recommend action to optimize combustion efficiency. The system includes a knowledge base containing rules for diagnosing the condition of major package boiler components. Minimum system requirements for MAD are an IBM-compatible AT-class personal computer (PC) with 640K base memory and 1 megabyte extended memory, 1.5 megabytes of free hard drive space, a color graphics adaptor (CGA), and DOS 3.0 (or higher).

The growing cost of operating and maintaining its central heating plants (CHPs) has forced the Army to seek alternatives to traditional methods of running these facilities. Computer technology offers the potential to automate and assist in many tasks, such as in the diagnosis of equipment malfunctions and failures in Army CHPs. An automated diagnostic tool for heating plant equipment could lower the cost of human labor by freeing personnel for higher priority work. Automatic diagnosis of problems could also reduce downtime for repair, promote thermal efficiency, and improve on-line reliability. Researchers at the U.S. Army Construction Engineering Research Laboratories (USACERL) investigated the application of artificial intelligence (AI) using knowledge-based expert systems to the monitoring and diagnosing of CHP boiler operations. A prototype system (MAD) was developed to Monitor And Diagnose boiler failure or identify inefficient operation, and recommend action to optimize combustion efficiency. The system includes a knowledge base containing rules for diagnosing the condition of major package boiler components. Minimum system requirements for MAD are an IBM-compatible AT-class personal computer (PC) with 640K base memory and 1 megabyte extended memory, 1.5 megabytes of free hard drive space, a color graphics adaptor (CGA), and DOS 3.0 (or higher).

The growing cost of operating and maintaining its central heating plants (CHPs) has forced the Army to seek alternatives to traditional methods of running these facilities. Computer technology offers the potential to automate and assist in many tasks, such as in the diagnosis of equipment malfunctions and failures in Army CHPs. An automated diagnostic tool for heating plant equipment could lower the cost of human labor by freeing personnel for higher priority work. Automatic diagnosis of problems could also reduce downtime for repair, promote thermal efficiency, and improve on-line reliability. Researchers at the U.S. Army Construction Engineering Research Laboratories (USACERL) investigated the application of artificial intelligence (AI) using knowledge-based expert systems to the monitoring and diagnosing of CHP boiler operations. A prototype system (MAD) was developed to Monitor And Diagnose boiler failure or identify inefficient operation, and recommend action to optimize combustion efficiency. The system includes a knowledge base containing rules for diagnosing the condition of major package boiler components. Minimum system requirements for MAD are an IBM-compatible AT-class personal computer (PC) with 640K base memory and 1 megabyte extended memory, 1.5 megabytes of free hard drive space, a color graphics adaptor (CGA), and DOS 3.0 (or higher).

Expert Systems With Applications 1991: Volume 3 , Issue CONTENTS. Digitized from IA1652627-07 . Previous issue: sim_expert-systems-with-applications_1991_2_4 . Next issue: sim_expert-systems-with-applications_1991_3_1 .

New paradigms for failure hypothesis formation are considered. An automated maintenance manual is developed and discussed. (Author)

New paradigms for failure hypothesis formation are considered. An automated maintenance manual is developed and discussed. (Author)

An expert system-based computer assisted design (CAD) package for propellant formulation design has been created for use on an Intel 80286/80386- based workstation (PC). This CAD package is intended for use by propellant formulators as a tool to aid in the design of, and properties estimation for, new rocket and gun propellants. A backward-chaining expert system shell (Level 5), dBase III-compatible databases, and a PC version of the NASA-Lewis thermochemical code CET89 are linked together into one package. Properties estimation is accomplished via the use of both the expert system shell and the CET89 code; the thermochemical code options RKT=T (rocket calculation) and UV=T (constant volume) are supported. The initial version of this expert system has been implemented for a propellant formulation consisting of three ingredients (binder, plasticizer, and energetic material). Current research efforts are going into the design of a more comprehensive package for use with up to 21 propellant ingredients.

It is our intention here only to discuss the nature, complexity and tools concerning the design of Smart Help, an expert help facility for aiding users of Computer Algebra Systems. Although the expert help system presented here has been particularly oriented to REDUCE (as a consequence of our former experience with this system), we point out that the concept of Smart Help can be extended to other Computer Algebra Systems. Technically, Smart Help is a Production System on the top of a particular implementation of MANTRA, a hybrid knowledge representation system, which has REDUCE integrated as an additional knowledge representation module. Since the heuristic level of MANTRA has not yet been implemented, being presently represented by the Lisp language itself, Smart Help is coded in Lisp and resides in the same Lisp session of MANTRA. A prototype of Smart Help is now running on a SUN work-station on an experimental basis.

It is our intention here only to discuss the nature, complexity and tools concerning the design of Smart Help, an expert help facility for aiding users of Computer Algebra Systems. Although the expert help system presented here has been particularly oriented to REDUCE (as a consequence of our former experience with this system), we point out that the concept of Smart Help can be extended to other Computer Algebra Systems. Technically, Smart Help is a Production System on the top of a particular implementation of MANTRA, a hybrid knowledge representation system, which has REDUCE integrated as an additional knowledge representation module. Since the heuristic level of MANTRA has not yet been implemented, being presently represented by the Lisp language itself, Smart Help is coded in Lisp and resides in the same Lisp session of MANTRA. A prototype of Smart Help is now running on a SUN work-station on an experimental basis.

As evidenced by current literature, there appears to be a continued interest in the study of real-time expert systems. It is generally recognized that speed of execution is only one consideration when designing an effective real-time expert system. Some other features one must consider are the expert system's ability to perform temporal reasoning, handle interrupts, prioritize data, contend with data uncertainty, and perform context focusing as dictated by the incoming data to the expert system. This paper presents a strategy for implementing a real time expert system on the iPSC/860 hypercube parallel computer using CLIPS. The strategy takes into consideration not only the execution time of the software, but also those features which define a true real-time expert system. The methodology is then demonstrated using a practical implementation of an expert system which performs diagnostics on the Space Shuttle Main Engine (SSME). This particular implementation uses an eight node hypercube to process ten sensor measurements in order to simultaneously diagnose five different failure modes within the SSME. The main program is written in ANSI C and embeds CLIPS to better facilitate and debug the rule based expert system.

Expert Systems With Applications 1990: Volume 1 , Issue CONTENTS. Digitized from IA1652627-07 . Next issue: sim_expert-systems-with-applications_1990_1_index .

Expert Systems with Applications 1991: Volume 2 , Issue Contents. Digitized from IA1646427-01 . Previous issue: sim_expert-systems-with-applications_1990_1_4_0 . Next issue: sim_expert-systems-with-applications_1991_2_1_0 .

Expert Systems With Applications 1995: Volume 9 , Issue CONTENTS. Digitized from IA1652627-07 . Previous issue: sim_expert-systems-with-applications_1995_8_4 . Next issue: sim_expert-systems-with-applications_1995_9_1 .

Current research in the application of expert systems to problems in the flight research environment is discussed. In what is anticipated to be a broad research area, a real time expert system flight status monitor has been identified as the initial project. This real time expert system flight status monitor is described in terms of concept, application, development, and schedule.

Encore: Original airdate 4-24-2023. On today's 'BradCast': The multi-state criminal plot by MAGA conspiracists to breach voting system software after the 2020 election actually began in Trump's Oval Office and may have been overseen by Trump himself. The blockbuster new revelations add to our previous reporting regarding the unlawful, criminal breaches of sensitive voting and tabulation software by MAGA rightwingers following the 2020 election in right-leaning Coffee County, Georgia. SUSAN GREENHALGH of Free Speech for People explains her discovery that the plot to breach voting systems in several states was hatched during an infamous Oval Office meeting in December 2020, presided over by Donald Trump. Greenhalgh also explains new reporting on recently uncovered text messages of two MAGA conspiracists discussing whether to use the information from the breached systems to prevent certification the victory of Democratic Sen. Jon Ossoff, or 'hold it for a bigger moment.' Yet to date there is no evidence that either the FBI or the Justice Department are investigating the multi-state conspiracy. Greenhalgh discusses how the stolen software can be used to attack upcoming elections, both directly and indirectly, why federal investigations are needed into the security breach and Georgia officials' lackadaisical response, and critical actions to secure elections going forward.

The search for new, innovative approaches to training has led the U.S. Army Simulation, Training and Instrumentation Command (STRICOM) in Orlando, Florida to develop a new family of high technology, computer-based training facilities called the Combined Arms Tactical Trainer (CATT). These training facilities will support realistic, interactive, combined arms training involving army units at the same or different locations operating in a virtual environment on a digitized battlefield. The first of the CATT family to be developed will be the Close Combat Tactical Trainer facility featuring manned simulator modules for training armored and mechanized forces at battalion and below. Two versions of the CCTT system are under development: fixed-site system and mobile systems. Figure 1 below depicts the general layout and major components of a fixed-site CCTT facility. The manned simulator modules of the CCTT include main battle tanks, armored personnel carriers and command and control vehicles. The operations center emulates and controls battlefield operating systems that habitually train with the unit undergoing training. The after action review consoles replay segments of a training scenarios for the training unit to illustrate training lessons. The master control console starts and ends the training scenarios, and controls the tempo of the training scenario as well. The maintance console monitors the systems and conducts system diagnostics. There are also consoles for controlling the computer generated, and semiautomatic forces that populate the digitized battlefield during scenario execution.

This paper describes a framework for running cooperating agents in a distributed environment to support the Intelligent Computer Aided Design System (ICADS), a project in progress at the CAD Research Unit of the Design Institute at the California Polytechnic State University. Currently, the systems aids an architectural designer in creating a floor plan that satisfies some general architectural constraints and project specific requirements. At the core of ICADS is the Blackboard Control System. Connected to the blackboard are any number of domain experts called Intelligent Design Tools (IDT). The Blackboard Control System monitors the evolving design as it is being drawn and helps resolve conflicts from the domain experts. The user serves as a partner in this system by manipulating the floor plan in the CAD system and validating recommendations made by the domain experts. The primary components of the Blackboard Control System are two expert systems executed by a modified CLIPS shell. The first is the Message Handler. The second is the Conflict Resolver. The Conflict Resolver synthesizes the suggestions made by the domain experts, which can be either CLIPS expert systems, or compiled C programs. In DEMO1, the current ICADS prototype, the CLIPS domain expert systems are Acoustics, Lighting, Structural, and Thermal; the compiled C domain experts are the CAD system and the User Interface.

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could affect the output from a set of rules.

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could affect the output from a set of rules.

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could affect the output from a set of rules.

An expert system is a computer program at the level of a human expert in a complex but narrow field. Two types of expert systems which involve statistical expertise are statistical consulting programs and programs which find patterns in databases. Consulting programs can now be built quickly using programming tools. Twosamp, a consultant for advising on the analysis of univariate two-sample problems, was constructed in less than a week using one such tool, EMYCIN, Systems like Twosamp cannot model the initial stages of a consulting session, but can provide expert assistance on a class of models has been selected. Consultants and pattern-finders can assist statisticians in building models, generating hypotheses, and maintaining complex databases. As well, they can serve as laboratories for experimentation with statistical strategies. Expert systems also use statistical expertise. Most systems include mechanisms for reasoning under uncertainty. Methods under investigation include fuzzy logic, Dempster-Shafer theory, Bayesian analysis and various ad hoc methods. Learning systems use statistics to infer inductive rules. As well, statistical reasoning will be used to evaluate the performance of expert systems.

This thesis examines the organizational causes of the Department of Defense's (DoD) inability to acquire working defense systems. One major cause of this is identified as a lack of a sufficient number of trained and experienced acquisition personnel. An examination of the definitions of Decision Support and Expert Systems is made to determine their suitability for application to this problem. The information system framework of Gorry and Scott Morton is used to structure the acquisition problem. The DoD acquisition problem is found to be a good candidate for the application of expert systems. An expert system architecture is developed to provide acquisition personnel both technical and management support. Use of a central mainframe, connected to the Defense Data Network will provide nationwide access, with centralized control of the knowledge base. The architecture allows for the incorporation of existing conventional software under expert software control. In order to reduce development cost and time, the use of existing DoD manuals, as the knowledge base, is proposed. A prototype module, utilizing the M.1 expert shell and DoD manual 4245.7-M and NAVSO P-6071 is developed to prove the feasibility of this approach.

This thesis examines the organizational causes of the Department of Defense's (DoD) inability to acquire working defense systems. One major cause of this is identified as a lack of a sufficient number of trained and experienced acquisition personnel. An examination of the definitions of Decision Support and Expert Systems is made to determine their suitability for application to this problem. The information system framework of Gorry and Scott Morton is used to structure the acquisition problem. The DoD acquisition problem is found to be a good candidate for the application of expert systems. An expert system architecture is developed to provide acquisition personnel both technical and management support. Use of a central mainframe, connected to the Defense Data Network will provide nationwide access, with centralized control of the knowledge base. The architecture allows for the incorporation of existing conventional software under expert software control. In order to reduce development cost and time, the use of existing DoD manuals, as the knowledge base, is proposed. A prototype module, utilizing the M.1 expert shell and DoD manual 4245.7-M and NAVSO P-6071 is developed to prove the feasibility of this approach.

A prototype expert system was developed for maintaining autonomous operation of a Mars oxygen production system. Normal operation conditions and failure modes according to certain desired criteria are tested and identified. Several schemes for failure detection and isolation using forward chaining, backward chaining, knowledge-based and rule-based are devised to perform several housekeeping functions. These functions include self-health checkout, an emergency shut down program, fault detection and conventional control activities. An effort was made to derive the dynamic model of the system using Bond-Graph technique in order to develop the model-based failure detection and isolation scheme by estimation method. Finally, computer simulations and experimental results demonstrated the feasibility of the expert system and a preliminary reliability analysis for the oxygen production system is also provided.

Includes bibliographical references and index

Includes bibliographical references (pages 261-262) and index

Within the U.S. Army Corps of Engineers (USACE) military construction program, a limited number of quality assurance/quality control (QA/QC) personnel are responsible for an increasingly large workload involving many more complex practices than found in traditional construction. To ensure the continued quality of military facilities, several approaches are being considered, including automation. Expert systems technology in particular shows great promise in creating tools to assist QA/QC elements. Past development of expert systems has shown that the user must become involved early in the process to ensure suitable system performance. However, in developing some systems, the user is not known and the system cannot be tailored for a particular level of domain knowledge. When this situation occurs, it is necessary to provide flexibility in a system to handle users with differing levels of knowledge about the domain. Incorporating this flexibility into an expert system is a major problem in current expert system development and different approaches have been tried to deal with the problem. This report describes the development of decision models to allow flexibility in handling multilevel user knowledge about a specific domain. These models can be implemented in many different commercially available shells to supplement the available options and provide a more flexible interface for expert system users.

Within the U.S. Army Corps of Engineers (USACE) military construction program, a limited number of quality assurance/quality control (QA/QC) personnel are responsible for an increasingly large workload involving many more complex practices than found in traditional construction. To ensure the continued quality of military facilities, several approaches are being considered, including automation. Expert systems technology in particular shows great promise in creating tools to assist QA/QC elements. Past development of expert systems has shown that the user must become involved early in the process to ensure suitable system performance. However, in developing some systems, the user is not known and the system cannot be tailored for a particular level of domain knowledge. When this situation occurs, it is necessary to provide flexibility in a system to handle users with differing levels of knowledge about the domain. Incorporating this flexibility into an expert system is a major problem in current expert system development and different approaches have been tried to deal with the problem. This report describes the development of decision models to allow flexibility in handling multilevel user knowledge about a specific domain. These models can be implemented in many different commercially available shells to supplement the available options and provide a more flexible interface for expert system users.

Within the U.S. Army Corps of Engineers (USACE) military construction program, a limited number of quality assurance/quality control (QA/QC) personnel are responsible for an increasingly large workload involving many more complex practices than found in traditional construction. To ensure the continued quality of military facilities, several approaches are being considered, including automation. Expert systems technology in particular shows great promise in creating tools to assist QA/QC elements. Past development of expert systems has shown that the user must become involved early in the process to ensure suitable system performance. However, in developing some systems, the user is not known and the system cannot be tailored for a particular level of domain knowledge. When this situation occurs, it is necessary to provide flexibility in a system to handle users with differing levels of knowledge about the domain. Incorporating this flexibility into an expert system is a major problem in current expert system development and different approaches have been tried to deal with the problem. This report describes the development of decision models to allow flexibility in handling multilevel user knowledge about a specific domain. These models can be implemented in many different commercially available shells to supplement the available options and provide a more flexible interface for expert system users.

Bibliography: p. 27-28