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by the human insight of an experienced designer (knowledge engineer) in order to
create an Expert System. I will also demonstrate in this chapter why our current
systems are limited and how this view will stimulate new frontiers of research.
The study of knowledge is usually referred to as epistemology, but this study is
primarily concerned with the nature of people and their relationship to the world.
It is not expressed in terms of computer emulations. In particular, the traditional
concern is centred on the justification of knowledge. The difficulty in obtaining an
absolutely confident answer to the question of whether a particular set of beliefs
are 'true' can be seen through the works of the French philosopher Rene Descartes
(see Sutcliffe
1968
). He, having considered the possibility that even ones own senses
are suspect (we could be living in a dream, or hallucinating, or God could be playing
a game), was reduced to starting his philosophies with the unconfirmed belief that
God in His benevolence would not deliberately mislead us. That is, our observations
are directly related to what is 'True'.
Truth has thus been taken to be God's view of His world. Ironically, from this
underlying foothold of belief in God, formal representations of knowledge, such as
logic and predicate calculus have been created, both of which in their turn underwrite
the technology of the computer and the modern theories of artificial intelligence.
Certainly ever since the philosopher Immanuel Kant established that knowledge
depends upon our concepts it has been recognised that knowledge is inseparable
from the mode of its representation.
A 'knowledge engineer' is an expert who can elicit and thus capture the knowledge
of a target expert in a way that can be used by an Expert System. A knowledge engineer
will conceptualise expert knowledge in terms of their preferred method. It should be
noted that there is no standard method of representation at this stage of knowledge
capture. The process involves many hours of informal and semiformal discussions
between a knowledge engineer and the target expert or experts. The results of these
discussions are often sketched in terms of diagrams and a semi-formal language.
However, since there is a strong dependency between representation and knowledge,
such semiformal languages are developed within an engineering environment and
are normally biased towards a particular view of a chosen Expert System design
(Shaw and Woodward
1990
). Because of this bias such representations obscure
certain aspects of knowledge; in particular, the different roles of different kinds
of knowledge. It is therefore important to step away from the Expert System and
construct a general theory of knowledge representation. This representation should
be cast to lie outside any particular Expert System design but appropriate to be used
for all of them.
4.2
A Taxonomic Approach
The question “What is a knowledge representation?” was answered in part by Randal
Davis et al. (
1993
), and a summary of his team's deliberations is shown in Table
4.1
.
The Roles of Knowledge Representation: (Davis et al.
1993
). The important issue
