Abstract
New system architectures, such as model-based reasoning and neural networks, have increased the difficulty of expert system design specification. In this paper, I suggest that to identify the appropriate subtask allocation and expert system architecture, certain preliminary questions should be asked and the data evaluated. The front-end analysis described here is a framework loosely based on three levels of human cognition; analytical, rule-based, and implicit processing. Keeping these different types of cognitive task performance in mind, the framework specifies a set of factors to evaluate in the front-end analysis. Once data is collected for these factors, it is possible to evaluate whether an expert system should be designed for each specific subtask, and if so, what type of system architecture should be implemented. A suggested guideline for architecture choice is presented.
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