Downloadable - About University

430 Alternative decision-support systems
textbook study, students are unable to show diagnostic expertise. Thisis
achieved from an ‘apprenticeship period’ where they observe experts in
real diagnoses and attempt to duplicate the skill by practicing themselves.
Indeed, expert knowledge consists of many unwritten ‘rules of
thumb’:
[it is] ...largely heuristic knowledge, experimental, uncertain – mostly ‘good
guesses’ and ‘good practice’, in lieu of facts and figures. Experience has also
taught us that much of this knowledge is private to the expert, not because he
is unwilling to share publicly how he performs, but because he is unable to.
He knows more than he is aware of knowing ...What masters really know
is not written in the textbooks of the masters. But we have learned that this
private knowledge can be uncovered by the careful, painstaking analysis of
a second party, or sometimes by the expert himself, operating in the context
of a large number of highly specific performance problems.
Sometimes in order to understand one expert’s actions the expertise of
another is required. For example, in organized human/machine chess
matches a high-ranking player is often present in order to explain
the likely reason for each player’s moves. Similarly, in eliciting medical
expertise a doctor can be employed to observe a patient–doctor interview
and infer the reasons for questions asked of the patient.
Given the ‘hidden’ nature of expert knowledge, it is not surprising
to find research in the area of knowledge engineering pointing to the
difficulties of elicitation. Hayes-Roth et al. 2 have described it as a ‘bottleneck
in the construction of expert systems’. For example, communication
problems arise because not only is the knowledge engineer relatively
unfamiliar with the expert’s area or ‘domain’ but the expert’s vocabulary
is often inadequate for transferring expertise into a program. The
‘engineer’ thus plays an intermediary role with the expert in extending
and refining terms. Similarly, Duda and Shortcliffe 3 conclude that:
The identification and encoding of knowledge is one of the most complex
and arduous tasks encountered in the construction of an expert system ...
Thus the process of building a knowledge base has usually required an AI
researcher. While an experienced team can put together a small prototype in
one or two man-months, the effort required to produce a system that is ready
for serious evaluation (well before contemplation of actual use) is more often
measured in man-years.
Wilkins et al. 1 reinforce this view and note that attempts to automate
the ‘tedious’ and ‘time-consuming’ process of knowledge acquisition
between expert and ‘engineer’ have so far proved unsuccessful. It is clear