A "causal" expert system based on hypothetical reasoning and its application to a Mark 45 turret gun's lower hoist are described. HOIST is a system that performs fault diagnosis without the use of a domain expert or "shallow rules". Rather its "knowledge" is coded directly from a structural specification of the Mark 45 lower hoist. The technology reported here for assisting the lesser acquainted diagnostician differs considerably from the normal rule-based expert system techniques: it reasons about machine failures from a functional model of the device. In a mechanism like the lower hoist, a functional model must reason about forces, fluid pressures and mechanical linkages, that is, qualitative physics. HOIST technology can be directly applied to any exactly specified device for modeling and diagnosis of single or multiple faults. Hypothetical reasoning, the process embodied in HOIST, has general utility in qualitative physics and reason maintenance. / Master of Science

No description available.

Usability of an expert system is dependent upon the relationship between a human user and the expert system interface. The interface is defined as any combination of equipment with which the user and expert system communicate. Within the context of this research, the interface is considered to be the expert system text and graphics appearing on display hardware. This type of interface is known as an advisor. A state transition model is used to represent human-advisor interaction. The model provides a mechanism by which to collect objective human performance data. In addition, it is used to specify human-advisor interaction metrics. To test the state transition model, an expert system, Function Diagnostic, was developed. Function Diagnostic determines mathematical expressions for the graphical representation of selected piecewise linear and polynomial function. An experiment was performed in which subjects used Function Diagnostic to solve problems. Each problem was associated with one of three levels of difficulty: easy, moderate, and hard. The subject population consisted of 36 engineering students. Two subjective measures were recorded: (1) user confidence in solutions reached by Function Diagnostic and (2) user perception of problem difficulty. Objective measures associated with user errors and problem solving skills were also recorded. Expert system usability measures were derived from the human-advisor interaction metrics, and these measures are incorporated into a usability function. The usability function is a linear combination of (1) subjective measures and (2) the usability measures derived from the human-advisor interaction metrics. The function can be used to predict how a usability score will be changed when function variables are perturbed. / Ph. D.

Ph. D.

by Leung Wing-shan, Queenie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 79-82). / Abstracts in English and Chinese. / Abstract --- p.iii / Acknowledgements --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Current Problem --- p.4 / Chapter 1.2 --- Research Proposal --- p.6 / Chapter 1.3 --- Research Target --- p.8 / Chapter 1.4 --- Thesis Overview --- p.9 / Chapter 2 --- Fuzzy Logic and Fuzzy Petri Net --- p.11 / Chapter 2.1 --- Background --- p.13 / Chapter 2.2 --- Fuzzy sets --- p.15 / Chapter 2.3 --- Operations on fuzzy sets --- p.18 / Chapter 2.4 --- Fuzzy logic --- p.20 / Chapter 2.5 --- Weighted Fuzzy Petri Net --- p.23 / Chapter 2.6 --- Fuzzy reasoning --- p.25 / Chapter 2.7 --- More about fuzzy logic --- p.29 / Chapter 2.8 --- Chapter Summary --- p.31 / Chapter 3 --- Dynamic Certainty Factor --- p.32 / Chapter 3.1 --- Definition --- p.32 / Chapter 3.1.1 --- Background --- p.33 / Chapter 3.1.2 --- Examples --- p.37 / Chapter 3.2 --- Advantages --- p.43 / Chapter 3.2.1 --- Best reasoning --- p.43 / Chapter 3.2.2 --- Independency --- p.46 / Chapter 3.2.3 --- Interaction effect --- p.49 / Chapter 3.3 --- Chapter Summary --- p.51 / Chapter 4 --- Experiment --- p.53 / Chapter 4.1 --- Transformation Definition --- p.54 / Chapter 4.2 --- Case Study --- p.61 / Chapter 4.2.1 --- Example 1 --- p.61 / Chapter 4.2.2 --- Example 2 --- p.63 / Chapter 4.3 --- Analysis --- p.65 / Chapter 4.3.1 --- Comparisons --- p.65 / Chapter 4.3.2 --- Discussion --- p.67 / Chapter 4.4 --- Chapter Summary --- p.68 / Chapter 5 --- Conclusion --- p.69 / Chapter 5.1 --- Final Summary --- p.69 / Chapter 5.2 --- Deficiency and Improvement --- p.71 / Chapter 5.3 --- Future Research Aspect --- p.73 / Appendix --- p.75 / Chapter A --- Data Details --- p.76 / Bibliography --- p.79

Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained. However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations. In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants. The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components. The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts. The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose. The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2008. / Includes bibliographical references.

MCom (Business Information Systems) / Deparment of Business Information Systems / The extent of success of a given project can be increased by using an appropriate Project Management Methodology (PMM) that takes into account the specific characteristics of the project (such as complexity, size, budget, nature of risk, etc.). PMMs have evolved over the years to become more diverse, complex, with evolving and dynamic ICT platforms. Such PMMs have traditionally been used as frameworks to guide the project management process for decision makers (such as Project Managers, Project Owners and Project Teams). The choice of selecting an appropriate project methodology is daunting; apart from other considerations related to project characteristics such as budget, scope, schedule, performance and resource constraints. One of the vital stages of a successful software development project is selecting a good software development methodology that best suits that project. The aim of this research is to investigate the critical factors to be considered by project managers in the selection of the software development methodology for the project. These critical factors are then used as a foundation for an architecture for an “unbounded rule-based expert system. A survey was conducted amongst project managers to determine the critical factors necessary for the selection of a software development methodology. From the findings of the study, it was established the critical factors revolved around three constructs of Project Excellence Enablers, Excellent Project Management Practices, and Business Value Proposition factors. The findings from this study therefore provided a rationale and a basis for the evolution of an “Unbounded Rule-Based Expert Systems Architecture” as a basis for the selection of the right software development methodology / NRF

Computer systems for the interpretation of diagnostic ECGs are widely used, but currently provide no explanatory or teaching functions of value to the less experienced practitioner. The relevant literature is reviewed, and specifications are provided for an ECG analysis system which will function as a learning aid for undergraduate and postgraduate medical students. Key aspects of the specifications are implemented on an IBM-PC. Recommendations for further development are provided.

No description available.