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Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Today's business environment is a highly competitive marketplace. In this competition, organizations distribute numerous rewards to motivate, attract and retain employees, such as pay, fringe benefits and promotions. However, not all managers have the necessary knowledge and expertise to effectively decide and structure reward systems. This thesis presents an expert system to assist managers with designing the most appropriate reward system in their organizations. The system queries the user about the organization's goals, structure, culture, technology and its management's vision. This information is then filtered through decision matrixes in the knowledge base to generate the results along with an explanation and an estimated accuracy factor. The system was designed and programmed using Microsoft Visual Basic 6.0. The decision tables in the knowledge base were designed and structured using a Microsoft Access database. The results show that similar knowledge base expert systems could be designed and programmed to assist managers for other purposes in organizations

Tri 1 – 2018/2019

The Spacelab Data Processing Facility (SLDPF) is an integral part of the Space Shuttle data network for missions that involve attached scientific payloads. Expert system prototypes were developed to aid in the performance of the quality assurance function of the Spacelab and/or Attached Shuttle Payloads processed telemetry data. The Spacelab Input Processing System (SIPS) and the Spacelab Output Processing System (SOPS), two expert systems, were developed to determine their feasibility and potential in the quality assurance of processed telemetry data. The capabilities and performance of these systems are discussed.

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.

It is widely recognized that technologies based on artificial intelligence (AI), especially expert systems, can make significant contributions to the productivity and effectiveness of operations of information and knowledge intensive organizations such as NASA. At the same time, these being relatively new technologies, there is the problem of transfering technology to key personnel of such organizations. The problems of examining the potential of expert systems and of technology transfer is addressed in the context of human factors applications. One of the topics of interest was the investigation of the potential use of expert system building tools, particularly NEXPERT as a technology transfer medium. Two basic conclusions were reached in this regard. First, NEXPERT is an excellent tool for rapid prototyping of experimental expert systems, but not ideal as a delivery vehicle. Therefore, it is not a substitute for general purpose system implementation languages such a LISP or C. This assertion probably holds for nearly all such tools on the market today. Second, an effective technology transfer mechanism is to formulate and implement expert systems for problems which members of the organization in question can relate to. For this purpose, the LIghting EnGineering Expert (LIEGE) was implemented using NEXPERT as the tool for technology transfer and to illustrate the value of expert systems to the activities of the Man-System Division.

ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) System. ARGES monitors and reduces data in real time from either the SAWD controller or a simulation of the SAWD assembly. It can detect gradual degradations or predict failures. This allows graceful shutdown and scheduled maintenance, which reduces crew maintenance overhead. Status and fault information is presented in a user interface that simulates what would be seen by a crewperson. The user interface employs animated color graphics and an object oriented approach to provide detailed status information, fault identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual faults. ARGES is seen as a forerunner of AI-based fault management systems for manned space systems.

Reasoning visualization is a useful tool that can help users better understand the inherently non-sequential logic of an expert system. While this is desirable in most all expert system applications, it is especially so for such critical systems as those destined for space-based operations. A hierarchical view of the expert system reasoning process and some characteristics of these various levels is presented. Also presented are Abstract Time Slice (ATS) displays, a tool to visualize the plethora of interrelated information available at the host inferencing language level of reasoning. The usefulness of this tool is illustrated with some examples from a prototype potable water expert system for possible use aboard Space Station Freedom.

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 90's cannot enjoy an increased level of autonomy without the efficient use of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real time demands are met for large expert systems. Speed-up via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial labs in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems was surveyed. The survey is divided into three major sections: (1) multiprocessors for parallel expert systems; (2) parallel languages for symbolic computations; and (3) measurements of parallelism of expert system. Results to date indicate that the parallelism achieved for these systems is small. In order to obtain greater speed-ups, data parallelism and application parallelism must be exploited.

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited.

This paper gives an overview of industrial applications of real-time knowledge based expert systems (KBES's) in the process industries. After a brief overview of the features of a KBES useful in process applications, the general roles of KBES's are covered. A particular focus is diagnostic applications, one of the major applications areas. Many applications are seen as an expansion of supervisory control. The lessons learned from numerous online applications are summarized.

This paper gives an overview of industrial applications of real-time knowledge based expert systems (KBES's) in the process industries. After a brief overview of the features of a KBES useful in process applications, the general roles of KBES's are covered. A particular focus is diagnostic applications, one of the major applications areas. Many applications are seen as an expansion of supervisory control. The lessons learned from numerous online applications are summarized.

Journal of Research of the National Bureau of Standards

The Space Shuttle uses a complex set of software to guide, navigate, and control it through all phases of flight. Adding to the complexity is the fact that the software is reconfigured for each flight, i.e., thousands of constants in the software are changed to reflect the unique properties of a given mission. In the last level of tests, the software is flown through end to end nominal and abort scenarios taking the shuttle from liftoff to landing. The analysis of the results of the testing is experience and labor intensive. A set of pass/fail criteria were defined for each test case and in parallel with the knowledge acquisition, tools were developed which allowed the automation of the knowledge being gathered on paper. A prototype of the Analysis Criteria Expert System (ACES) was put into production in the verification of the reconfigured onboard flight software.

The Naval Postgraduate School has developed a number of small expert system prototypes for the Naval Supply Systems Command (NAVSUP) to automate some functions in inventory management. These expert systems were developed during the last several years to aid inventory managers at Navy Stock Points. Several thesis students have successfully developed three separate standalone functioning and employable systems which run on MS-DOS based machines, and which use different knowledge representation approaches and different programming languages. Since these prototypes have been built, new expert system shells have become available. Because of advances in technology and the drive toward integration today, integration of these prototypes is important to enhance man-machine interface, increase system performance, and facilitate maintenance tasks. This thesis addresses the generic requirements needed to convert, integrate, and maintain the rule bases of three standalone expert systems and combine them into one functioning integrated expert system. It then provides such a system in a VP-Expert shell and describes the specific details of the conversion effort. Improvements needed are also discussed.

The article presents an analysis of the architecture of expert systems for cancer diagnosis based on modern artificial intelligence approaches. The key components of such systems are considered, including the knowledge base, inference mechanisms, user interface, and integration with multi-omic medical data. A comparative analysis of architectural solutions used in modern expert systems is carried out, and their advantages and limitations for early diagnosis and monitoring of cancer are identified. Particular attention is paid to machine learning methods that provide automatic knowledge updating and improve diagnostic accuracy. The results obtained can be used for further development of efficient, self-learning expert systems aimed at improving the quality of medical diagnosis and monitoring of patients with cancer. 

The article presents an analysis of the architecture of expert systems for cancer diagnosis based on modern artificial intelligence approaches. The key components of such systems are considered, including the knowledge base, inference mechanisms, user interface, and integration with multi-omic medical data. A comparative analysis of architectural solutions used in modern expert systems is carried out, and their advantages and limitations for early diagnosis and monitoring of cancer are identified. Particular attention is paid to machine learning methods that provide automatic knowledge updating and improve diagnostic accuracy. The results obtained can be used for further development of efficient, self-learning expert systems aimed at improving the quality of medical diagnosis and monitoring of patients with cancer. 

The article presents an analysis of the architecture of expert systems for cancer diagnosis based on modern artificial intelligence approaches. The key components of such systems are considered, including the knowledge base, inference mechanisms, user interface, and integration with multi-omic medical data. A comparative analysis of architectural solutions used in modern expert systems is carried out, and their advantages and limitations for early diagnosis and monitoring of cancer are identified. Particular attention is paid to machine learning methods that provide automatic knowledge updating and improve diagnostic accuracy. The results obtained can be used for further development of efficient, self-learning expert systems aimed at improving the quality of medical diagnosis and monitoring of patients with cancer. 

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Includes bibliographical references and index

Expert Systems with Applications 1991: Volume 3 , Issue Contents. Digitized from IA1646427-01 . Previous issue: sim_expert-systems-with-applications_1991_2_4_0 . Next issue: sim_expert-systems-with-applications_1991_3_1_0 .

This article is from BMC Health Services Research , volume 14 . Abstract None

Expert systems can be used to aid decisionmaking. A computerized adaptive test is one kind of expert system, although not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines expert systems reasoning and sequential probability ratio test stopping rules. Two versions of EXSPRT were developed, one with random selection of items (EXSPRT-R) and one with intelligent selection (EXSPRT-I). Two empirical studies were conducted in which these two new methods were compared to the traditional SPRT and to an adaptive mastery testing (AMT) approach based on item response theory (IRT). The EXSPRT-I tended to be more efficient than the AMT, EXSPRT-R, and SPRT models in terms of average test lengths. Although further research is needed, the EXSPRT-I initially appears to be a strong alternative to both IRT- and SPRT-based adaptive testing when categorical decisions about examinees are desired. The EXSPRT-I is clearly less complex than IRT, both conceptually and mathematically. It also appears to require many fewer examinees to establish empirically a rule base when compared to the large numbers needed to eliminate parameters for item response functions in the IRT model. (20 references) (Author/BBM)

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI.

This report focuses upon the potential for a combination of multimedia and expert system technologies to address maintenance problems in apparel manufacturing. There is a recognized shortage of skilled maintenance workers in the apparel industry. The judicious application of multimedia expert systems technology can potentially alleviate the problems caused by the lack of skilled maintenance workers and therefore strengthen the competitive position of the US in apparel manufacturing. The specific objectives of this project were: (1) to assess the needs of the apparel industry relative to equipment maintenance; and (2) to assess the degree to which interactive video technology can be merged with expert systems to meet industry needs. Maintenance encompasses two primary functions: (1) diagnosis and replacement of faulty machine parts; and (2) adjustment of machine mechanisms to alter the performance in order to accommodate material of differing characteristics or new product specifications.

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.