Artificial Intelligence
Expert systems: perils and promise
Communications of the ACM
Knowledge acquisition for expert systems
Knowledge acquisition for expert systems
Expert systems: tools and applications
Expert systems: tools and applications
The scope and limitations of first generation expert systems
Future Generation Computer Systems
AI Expert
Putting expert systems to work
Harvard Business Review
The rise of the expert company
The rise of the expert company
Artificial Intelligence for MicroComputers
Artificial Intelligence for MicroComputers
Human Problem Solving
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The development and use of knowledge-based (expert) systems has grown dramatically across a broad range of industries. Yet despite its growing importance, the study of expert systems lacks a cohesive framework for differentiating and comparing expert systems initiatives across different applications and in different industrial settings. The problem for IS managers is that a system that works in one situation may ot be appropriate for another. This article presents a classification methodology for the systematic evaluation of a broad range of expert systems. Of primary concern in this study is the measurement of the complexity of such systems. Complexity in the area of expert systems consists of two basic dimensions. The first dimension is the complexity of the underlying knowledge residing with the key experts. The second dimension of the framework focuses on the complexity of the technology incorporated into a given system. This framework is then applied to a sample of 50 successfully developed knowledge-based systems. The results can be used as a foundation for generating research hypotheses and for development time, budget, staffing, organizational control, and organizational participation.