The Psychology of Expertise: Cognitive Research and Empirical AI

By Robert R. Hoffman | Go to book overview

14
Expert-Novice Differences and Knowledge Elicitation

Mícheál Foley and Anna Hart


Introduction

Some years ago it was stated that knowledge acquisition is a major bottleneck in the development of expert systems ( Feigenbaum & McCorduck, 1984), and this view is still propounded. Although it is not unusual for knowledge engineers to experience great difficulty in getting experts to verbalize and formalize their knowledge, there is another fundamental problem. Often the overall objective of knowledge elicitation is to construct a computer-based system. In such a case the usefulness of the system, and therefore of the knowledge modeled in it, is assessed at least in part by the system's users. Knowledge engineering therefore involves constructing models of knowledge that can be validated by experts and that prove useful to the intended users. This means that the knowledge elicitation process should be viewed in the context of system design, and not as mining out an expert's knowledge. As Kidd says ( 1987), the process should take into account the different classes of users who are likely to use the system, their requirements, and the types of knowledge they bring to the problem-solving process.

The importance of the usability of a system's knowledge is stressed by Wyatt and Speigelhalter (in press) in their proposed guidelines for evaluating medical decision aids. They make a clear distinction between the validity and accuracy of the knowledge, as assessed by expert authorities, and its usefulness to the users in a practical problem-solving environment. Expert systems are often intended for use in situations that involve judgment under uncertainty and so cannot be prove correct. In such cases the usability and effectiveness of the systems are of paramount importance.

Often, systems are designed for users who are relative novices in the domain compared with the experts who supply the knowledge. Thus, the design process should take into account the differences between novices and experts. Our program of research is designed to further a general understanding of the differences between experts and novices and to study the implications for knowledge engineering.

This chapter has two aims: One is to outline what is already known about expert-novice differences, and the other is to identify key issues for knowledge engineering. There are four main sections: The first presents evidence of the importance of expert-novice differences from expert systems literature and identifies shortcomings in common methods for system development. The second summarizes the published literature on expert-novice differences. In the third section we describe an informal study that was carried out as part of our research program, and the fourth section identifies outstanding issues for knowledge engineering.

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