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the hypothesis and returns a prediction of a new fact (e.g., the next number in the
series). The validation of the retroduced hypothesis is the function of induction that
ensures that the hypothesis is suitable for the purpose (e.g., the prediction is correct,
the calculation was not too complex, it fits all or most of the facts and the form of
the hypothesis is simple).
The interaction of the three forms of inference explains why, given that many
possible hypotheses can be generated, we tend to generate only a small set for
evaluation. In the Faraday simulations done by Gooding and Addis (see Chaps. 6
and 7, Addis et al.
1990
,
1992
; Gooding
1990a
,b,
1992
) one example of this appears
as the Construe-Experiment-Clarify (C-E-C) cycle. It should be possible to identify
different reasoning styles (or different qualities of intelligence) according to the mix
or range of different kinds of inference.
4.4
Knowledge Acquisition
The knowledge engineer's task is to represent and model expert knowledge. This
involves negotiating a path from the informal and pre-articulate state of an expert's
(or experts') knowledge towards a formal model of it. Each stage of this process has
its own representation scheme and associated techniques.
It was argued by Addis et al. (
1993
) that because a functional database language
(FDL) explicitly represents all the different declarative roles of knowledge, it will
therefore provide a formal representation that is best suited for an unbiased elicitation
method. They then argued that a graphical approach to formal modelling exploits
the advantages of visualisation and offers a median way between the extremes of
rules-oriented and data-intensive approaches. By combining the two argued points
in a Visual Functional Program (VFP language Clarity
2
) they provide the justification
that VFP is an ideal environment for knowledge acquisition.
4.4.1
Abstracting to a Representation
An emphasis on the linguistic form (i.e., text) in most knowledge representation
defers to the traditional philosophical bias towards propositional knowledge. In
practice, most conceptual modelling representations involve an essential picto-
rial or diagrammatic representation, even though these may be defined in terms
of a language. Systemic networks, semantic nets, SFD graphs, KADS diagrams
and repertory grids express conceptual models within a two-dimensional diagram.
Relational analysis uses a diagrammatic scheme to show the elementary items (at-
tributes, objects and entities) that are recognised by a business. However, many
2
A version of Clarity is available through the author or from http://www.clarity-support.co.uk/.
