Implementation Of Expert System Research Articles

Evaluation and Control of Marine Vessels Stability in Waves

This paper presents the research results achieved by means of the preuminary design study and implementation of a Pc-based Expert System for Safely Navigation. PESSR It consists in an advisory system related to the stability and motion of the vessels in waves, where environmental information and marine vehicle conditions are acquired by a set of sensors. A proper real time corrective action is taken by means of PESSN.

A fuzzy expert system for fault detection in statistical process control of industrial processes

Little work has previously been reported on the use of fuzzy logic within statistical process control when this is used for fault detection as part of quality control systems in industrial manufacturing processes. Therefore, the paper investigates the potential use of fuzzy logic to enhance the performance of statistical process control (SPC). The cumulative sum of the deviation in the monitored parameter is combined with the deviation in an attempt to discriminate between false alarms and real faults and, consequently, to improve the quality of the solution. Combinations of control rules are utilized and trained to cope with different inputs such that rejection of false alarms is achieved and quick detection of real faults is obtained. The design and implementation of this fuzzy expert system (FES) are presented, and a comparative rule based study is performed.

Computerized decision support in medical imaging.

Describes challenges in using image processing and automated feature extraction for improving diagnostic accuracy. The generalization of digital technology in medical imaging makes it possible to design computer-aided decision-support tools to help resolve the difficulties encountered by radiologists. It has already been demonstrated that image processing and automated feature extraction can help improve diagnostic accuracy in some applications. Future challenges are for the development of vision techniques tailored for a wider range of imaging modalities and pathologies the design of methods for the combination of several classifiers, and the implementation of expert systems incorporating both low-level feature extractors and high-level clinical knowledge. After evaluation, these tools will be integrated in PACS systems to highlight regions of interest and to provide clinicians with a documented second opinion. Then, such integrated systems can be used routinely in the clinical practice and teaching of radiology.

The rise and ‘fall’ of expert systems in medicine

This paper represents a personal perspective on the last 13 years of research in medical artificial intelligence and expert systems, and describes the progress of the discipline from its inception to its current status. The challenges faced by researchers are examined, and it is argued that (despite the claims) theoretical and technical limitations are not the major barriers to the successful implementation of medical artificial intelligence and expert systems, but rather more complex professional and organizational issues are at stake.

An expert supervisory system for a pilot WWTP

This paper presents the development and implementation of a Real-Time Expert System (RTES) for the supervision and control of a wastewater treatment pilot plant (WWTPP) with biological removal of organic matter and nutrients. The hardware architecture contains different supervision levels, including two autonomous process computers (plant control and analysers control) and a PLC, being the ES the top supervisory level. The RTES has been developed using a commercial, industrially validated RTES-development software G2™ (by Gensym Corp). It actuates as the master in a supervisory setpoint control (SSC) scheme and it is based on a distributed architecture. The knowledge is organised in several agents, representing the available knowledge for every sub-process of the WWTPP. In addition to these independent agents, a supervisor agent acts as the master of the multi-agent distributed system.

Modelling of operator heuristics in dispatch for security enhancement

This paper addresses the problem of dispatching active and reactive power on a large interconnected power system to maintain security. It models operator heuristics in the decision making process by means of fuzzy sets. Sensitivity analysis is then used to derive the necessary movements in control settings within a practical expert system that seeks a low cost, low number-of-controllers solution. Results are presented for a 20 machine, 100 bus reduced model of the UK national grid. Comparisons are shown with a linear programming approach and a conventional production rule based expert system implementation. The flexibility of the new approach is demonstrated whereby it can be used to model any number of operational criteria.

Coordination and communication issues in multi-agent expert system: concurrent configuration design advisor

The model of concurrent configuration design and the architecture of Concurrent Configuration Design Advisor (CCDA) are considered. The CCDA is developed as an open dynamic expert system of interacting agents. In the context of this article a configuration problem is defined as a decision-making procedure performed by concurrent processes (agents). The analysis of concurrent and sequential activities in this system is based on structural transformations (in the form of graph grammars) that permits the definition of correct criteria for data integrity and the consistency of configured project data model, and any dynamic changes in the project to be modeled conveniently. This approach also makes it possible to handle complex hierarchial data structures of real configured objects and to model communication and synchronization of decision making in a distributed expert system in a common formalism. The system to be configured is decomposed into structured objects, called fragments. The proposed model consists of three types of agents. D-agents are those capable of the object configuration design within the constraints on their attributes. To cope with agents' coordination, objects' consistency and data integrity problems, a special type of agent, facilitator or F-agent, is introduced. Finally, project assistants or A-agents are responsible for the user interface at the stage of object model definition. The discussion is illustrated with examples from the application domain of flexible manufacturing systems. Experimental results, current and future work on the expert system implementation are considered.

Hybrid toxicology expert system: architecture and implementation of a multi-domain hybrid expert system for toxicology

A hybrid expert system prototype using artificial neural networks (ANN) and classical rules has been developed for predicting toxicology of compounds. Modularity was a must for the architecture of the system. The study of chemicals was approached by establishing classes. When appropriate descriptors are calculated for the molecule, the ANN classifier assigns the chemical class to the compound. Then the toxic activity is quantitatively predicted of by one of the trained ANN in the system. After that, a qualitative prediction (active/non-active) is made by a rule-based system, calling only the correct knowledge base (KB) for the assigned class. This last step enabled us to give an explanation of the results. All the rules in the KBs have been obtained with automated learning techniques.

Improving the utilization of clinical laboratory tests.

Reimbursement policies for health care services are greatly diminishing in the U.S. and Western Europe. Hence, there is an increasing need for doctors and other care givers to reduce costs without compromising the quality of the care being delivered. The clinical laboratory is viewed as an area of high costs where significant reductions have been targeted. Efficient utilization of laboratory services can be achieved by elimination of the general health panel, removal of old tests or those that provide redundant information, a reduction in the use of standing orders, more judicious use of drug assays, acceptance of clinical practice guidelines, and use of reflex testing algorithms. New technologies such as DNA probes can substantially improve diagnostic efficiency. Point-of-care testing devices which have higher costs than incremental central laboratory expenses should only be used if they reduce overall operating expenses. Implementation of expert systems can make remaining tests more effective. Doctors and laboratorians must collaborate to achieve more efficient utilization practices.

The design and application of expert systems in medicine

We consider implementation of the ORIENTIR expert system in the field of medicine. We show that it is possible to use expert-system shells, and we present information on practical applications of them.

The Determinants of Success in Implementing an Expert System in State Government

There are few tasks that managers dread as much as employee discipline. Except for cases in which the offense is severe enough to warrant termination, a manager must continue to interact daily with the employee after a punishment has been imposed. When the employee must be fired, the manager often fears that a lawsuit may not be far behind. Thus, when faced with the need to discipline an employee, supervisors might welcome help in figuring out what to do. That is the purpose of the computer expert system called the Supervisor Assistance System, created by the Florida Department of Highway Safety and Motor Vehicles (hereafter referred to as the Florida Department of Motor Vehicles). With the Supervisor Assistance System, a supervisor can first consult with the computer to determine if the employee's offense is actionable under departmental rules. If it is, the supervisor responds to a series of questions about the employee and the details of the offense. The computer then generates one or more recommendations for suggested actions to discipline the employee, along with supportive information from personnel rules. An expert system--one application of artificial intelligence--is a computerized decision-making software that structures expertise in a specific area and emulates human decision-making. The software helps users make a logical decision given its knowledge base drawn from laws, regulations, and human expertise.(1) Public agencies have used expert systems for over a decade, and they are becoming more common for a wide variety of purposes (Leonard-Barton and Sviokla, 1988; Martin, 1991). Social service departments use expert systems to determine eligibility for food stamps or refugee assistance, law enforcement units access solved and unsolved burglary cases through a computerized network to get a profile of possible perpetrators of an unsolved crime, and water testing laboratories apply for state license through an expert system. Although expert systems began as stand-alone computer programs, large public agencies are integrating their expert systems into their information data bases, and thus users may not even recognize their expert system as such (Hedberg, 1995). Most studies of public sector expert systems conclude that their promise is great, as they offer users significant potential benefits: increased productivity, improved service delivery effectiveness, and better problem anticipation (Hadden, 1989; McGowan and Lombardo, 1986; Shangraw, 1987). However, such systems have proven difficult to implement and institutionalize (Hadden, 1989, 204; Hauser and Hebert, 1992; Rubin, 1986). Few empirical studies of expert system implementation have been undertaken to assess what determines the extent to which public-sector decision makers will use an expert system initiated within their organization.(2) This article develops a model of the determinants of the degree to which managers utilize an expert system, and tests the model by examining how managers in the Florida Department of Motor Vehicles react to their expert system. The results offer lessons on how to implement an expert system in an agency so that the organization can achieve the benefits of computer technology. The Florida Department of Highway Safety and Motor Vehicles Supervisor Assistance System The Florida Department of Motor Vehicles developed the Supervisor Assistance System to provide consultations for its Supervisors on handling some 45 different employee offenses that can result in disciplinary actions.(3) (See Appendix 1 for a list of all offenses.) For any type of offense, the Supervisor Assistance System asks questions about the incident under investigation as well as other related information, such as the employee's prior disciplinary record and work history. (As an illustration, Appendix 2 presents the specific questions that the system asks a supervisor for the offense absent without authorized leave. …

Exploring expert system success factors for business process reengineering

Business process reengineering (BPR) has become the buzzword representing dramatic changes to the business processes of organizations trying to quickly preempt or react to market opportunities and competition. Much of the changes are enabled by computer-based technology such as expert systems (ES) providing a unique opportunity to study significant implementations of the technology within a relatively short time. Eight ES implementation success factors proposed in the literature were empirically tested in this study in terms of their direct and indirect importance to the benefits from using ES in BPR. Sixty-two ES applications within E.I. Dupont de Nemours dealing with business process changes significant enough to be called BPR were used. Despite the relatively small sample size, four of the eight success factors were corroborated: user satisfaction with the ES, the difficulty of the business problem addressed, the degree of user involvement in the ES implementation process, and characteristics of the ES shells.

Empirically testing ES success factors in business process reengineering

Previously proposed success factors for Expert Systems implementation are field tested in the context of business process reengineering (BPR) projects. Due to its nature mimicking human expert behavior, ES technology applications in BPR provide a unique opportunity to study major organization changes within a relatively short time. Using Pearson correlations and multivariate regression analysis, eight ES implementation success factors proposed in the literature were empirically tested in this study in terms of their importance to the BPR benefits derived from the application. Sixty-two ES applications within E.I. Dupont de Nemours and Company dealing with business process changes significant enough to be called BPR were used. Despite the relatively small sample size, six of the eight success factors were corroborated: user satisfaction with the ES, the importance and difficulty of the business problem addressed, user attitudes toward ES technology and the particular ES project, the degree of user involvement in the ES implementation process, and the ES developer(s) skills.

A knowledge-based approach to safety evaluation for plant start-up

To improve the safety of plant start-up operation, a safety evaluation system has been developed. As a key component in an operational design support system, the evaluation system examines any potential hazards during start-up operation simulation. The evaluation system is integrated into an operational design methodology which designs operable processes by proposing alternatives, examining process safety and operability, and modifying operating procedures or plant structures. Issues for both methodology and implementation of a G2-based expert system are discussed. Finally, the system is applied to an industrial hydrodesulphurization process.

A Real-Time Expert System Shell with Progressive Reasoning, Temporal Reasoning and Fuzzy Logic

The paper describes the implementation of the Real-Time Expert System that combines regulator properties with RTES techniques like future state predictions and progressive reasoning. An algorithm is presented to calculate a real-time planning of agents with deadlines, using the progressive reasoning levels.

Factors important to expert systems success a field test

Some important determinants for Expert Systems (ES) implementation success are field tested. User satisfaction is used as the surrogate of ES success, affording some basis for inter-study comparison. To reduce possible confounding results that may occur due to interorganizational differences, a case study approach to data collection in a single company has been used. The company is E.I. DuPont de Nemours & Company, Inc., which since 1986 has implemented over 1200 ES within the organization. The results underline the importance of seven of the nine hypothesized determinants of ES success. One of the most important factors is increasing system usage by establishing end-user training programs which desensitize the potential user community to ES technology and demonstrate its potential as a business tool and as a source of job improvement. Another important factor is the selection of an appropriate shell which matches the business problem, as well as user and developer requirements. Last, the results show the need for user involvement in the ES development process.

Design of A Knowledge-based Welding Advisor

Expert system implementation can take numerous forms ranging form traditional declarative rule-based systems with if-then syntax to imperative programming languages that capture expertise in procedural code. The artificial intelligence community generally thinks of expert systems as rules or rule-bases and an inference engine to process the knowledge. The welding advisor developed at Sandia National Laboratories and described in this paper deviates from this by codifying expertise using object representation and methods. Objects allow computer scientists to model the world as humans perceive it giving us a very natural way to encode expert knowledge. The design of the welding advisor, which generates and evaluates solutions, will be compared and contrasted to a traditional rule- based system.

Validation of expert systems for innovation management: Issues, methodology, and empirical assessment

Faced with the complexities of managing new product development, most of us would welcome the support of a computer-based system that captures the knowledge and the reasoning capabilities of experts in our field. Considerable effort has been focused on the design and development of expert systems for applications such as new product management. However, design and development are only two steps on the path to successful implementation of a useful expert system. A rigorous validation process is essential for ensuring that the expert system performs as intended. Using the INNOVATOR expert system as an example, Sundaresan Ram and Sudha Ram propose and test a framework for validating expert systems designed for new product management. The proposed validation framework considers three aspects of the expert system: its knowledge acquisition methodology, its performance, and its utility. Validation of an expert system's knowledge acquisition methodology involves assessment of the knowledge sources used, the criteria for selecting human experts, and the methods used for knowledge acquisition. Using multiple sources improves the likelihood that the expert system will capture the necessary core knowledge. Similarly, selection of the experts who are to supply the knowledge used by the expert system should be based on reliable measures of new product expertise rather than ad hoc measures. The system's performance is evaluated through formal tests of the accuracy and the completeness of the knowledge base, the consistency and the accuracy of the decisions made by the system, and the reasoning process by which the system reaches its decisions. Such tests may involve direct examination of the system by experts, and Turing tests, which compare both the recommendations and the reasoning process of the system with those of selected experts. Both types of tests may involve experts from whom knowledge was acquired during the development of the system as well as experts who were not involved in the design and development of the system. Assessment of an expert system's utility focuses on user perceptions of system performance and utility as well as the design of the user interface. First, end-users must evaluate the relevance of the chosen problem domain. In other words, the validation process must verify that the expert system addresses an important problem that requires decision support tools. Second, the expert system must provide a logical, systematic approach to solving the problem. Finally, the expert system must provide a consistent, intuitive user interface.

Analysis, design, implementation, and deployment of a prototype maintenance advisor expert system for the MK92 fire control system

In an effort to meet the challenges presented by the fiscal realities of today's defense budget, the Department of Defense (DoD) is seeking to exploit technology that promises to decrease operating costs, while improving operational readiness. Efforts which reduce repair costs, system down time, and the reliance upon outside technical representative are of particular interest. The development of the MK92 Maintenance Advisor Expert System (MK92 MAES) is one such effort. This paper describes the design and development of the MK92 MAES for the diagnosis and repair of the MK92 MOD 2 fire control system deployed on U.S. Navy guided missile frigates. System development is presented in terms of an expert system life cycle model which includes a thorough cost/benefit analysis, a novel approach for knowledge acquisition, an implementation strategy using a visual expert system development environment, and a phased deployment strategy. The system was developed by faculty and graduate students at the Naval Postgraduate School in cooperation with the Naval Warfare Center, Port Hueneme Division.

Validation of Expert Systems for Innovation Management: Issues, Methodology, and Empirical Assessment

Faced with the complexities of managing new product development, most of us would welcome the support of a computer‐based system that captures the knowledge and the reasoning capabilities of experts in our field. Considerable effort has been focused on the design and development of expert systems for applications such as new product management. However, design and development are only two steps on the path to successful implementation of a useful expert system. A rigorous validation process is essential for ensuring that the expert system performs as intended.Using the INNOVATOR expert system as an example, Sundaresan Ram and Sudha Ram propose and test a framework for validating expert systems designed for new product management. The proposed validation framework considers three aspects of the expert system: its knowledge acquisition methodology, its performance, and its utility.Validation of an expert system's knowledge acquisition methodology involves assessment of the knowledge sources used, the criteria for selecting human experts, and the methods used for knowledge acquisition. Using multiple sources improves the likelihood that the expert system will capture the necessary core knowledge. Similarly, selection of the experts who are to supply the knowledge used by the expert system should be based on reliable measures of new product expertise rather than ad hoc measures.The system's performance is evaluated through formal tests of the accuracy and the completeness of the knowledge base, the consistency and the accuracy of the decisions made by the system, and the reasoning process by which the system reaches its decisions. Such tests may involve direct examination of the system by experts, and Turing tests, which compare both the recommendations and the reasoning process of the system with those of selected experts. Both types of tests may involve experts from whom knowledge was acquired during the development of the system as well as experts who were not involved in the design and development of the system.Assessment of an expert system's utility focuses on user perceptions of system performance and utility as well as the design of the user interface. First, end‐users must evaluate the relevance of the chosen problem domain. In other words, the validation process must verify that the expert system addresses an important problem that requires decision support tools. Second, the expert system must provide a logical, systematic approach to solving the problem. Finally, the expert system must provide a consistent, intuitive user interface.