A structured framework for efficient problem solving in diagnostic expert systems

Abstract Diagnosis in chemical processing plants is recognized as an activity in which efficiency is achieved through structure in both the knowledge and the problem-solving strategy. By exploiting this structure in diagnostic expert systems, an efficient methodology for navigating the solution space of possible plant malfunctions results. One approach to computationally describing this structure is in terms of a small, finite set of underlying tasks which comprise the diagnostic activity. Since the task descriptions are independent of a particular application, the integration of the tasks forms a framework which is generally applicable to diagnosis in the domain. Within the context of this approach, a framework for a diagnostic expert system in the chemical plant domain is shown to consist fundamentally of a primary task associated with plant sensors and an auxiliary task associated with product quality data. The framework provides a means of appropriately leveraging both compiled and model-based knowledge.

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