Towards an understanding of the role of experience in the evolution from novice to expert

Abstract

Two major factors seem to distinguish novices from experts. First, experts generally know more about their domain. Second, experts are better than novices at applying and using that knowledge effectively. Within AI, the traditional approach to expertise has concentrated on the first difference. Thus, “expert systems” research has revolved around extracting the rules experts use and developing problem solving methodologies for dealing with those rules. Unlike these systems, human experts are able to introspect about their knowledge and learn from past experience. It is this view of expertise, based on the second distinguishing feature above, that we are exploring. Such a view requires a reasoning model based on organization of experience in a long-term memory, and incremental learning and refinement of both reasoning processes and domain knowledge. This paper will present the basis for this view, the reasoning model it implies, and a computer program which begins to implement the theory. The program, called SHRINK, models psychiatrie diagnosis and treatment.