Expert System Architecture
An expert
system is a set of programs that manipulate encoded knowledge to solve problems
in a specialized domain that normally requires human expertise. An expert
system’s knowledge is obtained from expert sources and coded in a form suitable
for the system to use in its inference or reasoning processes. The expert
knowledge must be obtained from specialists or other sources of expertise, such
as texts, journal, articles and databases. This type of knowledge usually
requires much training and experience in some specialized field such as
medicine, geology, system configuration, or engineering design. Once a
sufficient body of expert knowledge has been auquired, it must be encoded in
some form, loaded into a knowledge base, then tested, and refined continually
throughout the life of the system
Characteristics Features of Expert Systems
Expert
systems differ from conventional computer system in several important ways
Expert
systems use knowledge rather than data to control the solution process. Much of
the knowledge used in heuristic in nature rather than algorithmic
The
knowledge is encoded and maintained as an entity separate from the aontrol
program. As such, it is not complicated together with the control program
itself. This permits the incremental addition and modification of the knowledge
base without recompilation of the control programs. Furthermore, it is possible
in some cases to use different knowledge bases with the same control programs
to produce different types of expert systems. Such systems are known as expert
system shells since they may be loaded with different knowledge bases
Expert
systems are capable of explaining how a particular conclusion was reached, and
why requested information is needed during a consultation. This is important as
it gives the user a chance to assess and understand the systems reasoning
ability, thereby improving the user’s confidence in the system
Expert
systems use symbolic representations for knowledge and perform their inference
through symbolic computations that closely resemble manipulations of natural
language
Expert
systems often reason with metaknowledge, that is, they reason with knowledge
about themselves, and their own knowledge limits and capabilities
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