A REVIEW OF RECENT DEVELOPMENTS RELATING TO DEEP KNOWLEDGE EXPERT SYSTEMS
Date
1986-03-01
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Abstract
Commercial exploitation of artificial intelligence has motivated
development of expert systems based largely on heuristic principles.
Recently, attention has been given to the use of embedded domain
models to augment the reasoning abilities of heuristics. One reason
for incorporating an explicit domain model is the belief that the
human expert's heuristics are founded in a deeper understanding of
the domain: that an expert's base of knowledge is more than simply a
large collection of empirical observations.
Secondly, acquisition of knowledge for heuristic expert systems has been
recognized as the bottleneck preventing wider-scale adoption of
expert system technology. It is hoped that domain models based on
established scientific knowledge can expedite the transfer of knowledge
from traditional scientific activities into symbolic form. The third
rationale behind the demand for embedded domain models is the
apparently arbitrary character of heuristics.
The term "deep knowledge" has been coined to describe knowledge that
derives from an explicit domain model. This usage parallels the
linguistic concepts of "deep" and "shallow" structure, distinguishing
the assumed internal representation of utterances and their verbal
expression.
This paper presents an overview of some recent literature about Deep
Knowledge. The subject matter of expert systems is divided into three
major area: representation, inference, and knowledge acquisition. After
discussing the expected benefits of deep knowledge, a sampling of
papers relevant to each area of interest is reviewed.
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Computer Science