A Multiagent Framework for a Diagnostic and Prognostic System
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A Multiagent Framework for a Diagnostic and Prognostic System Irtaza Barlas 124 Pages Directed By: Dr. George Vactsevanos The shortcomings of the current diagnostic and prognostic systems stem from the limitations of their frameworks. The framework is typically designed on the passive, open loop, static, and isolated notions of diagnostics, in that the framework does not observe its diagnostic results (open-looped), hence can not improve its performance (static). Its passivity is attributed to the fact that an external event triggers the diagnostic or prognostic action. There is also no effort in place to team-up the diagnostic systems for a collective learning, hence the implementation is isolated. In this research we extend the current approaches of the design and implementation of diagnostic and prognostic systems by presenting a framework based upon Multiagent systems. This research created novel architectures by providing such unique features to the framework, as learning, reasoning, and coordination. As the primary focus of the research the concept of Case-Based Reasoning was exploited to reason in the temporal domain to generate better prognosis, and improve the accuracy of detection as well as prediction. It was shown that the dynamic behavior of the intelligent agent helps it to learn over time, resulting in improved performance. An analysis is presented to show that a coordinated effort to diagnose also makes sense in uncertain situations when there are certain number of systems attempting to communicate certain number of failures, since there can be high probability of finding a shareable experience.