Page 185 - DCAP506_ARTIFICIAL_INTELLIGENCE

Unit 13: Expert Systems and its Architecture

expertise is encoded in both program and data structures. In the expert system approach all of Notes
the problem related expertise is encoded in data structures only; none is in programs. This
organization has several benefits.
An example may help contrast the traditional problem solving program with the expert system
approach.

Example: The example is the problem of tax advice. In the traditional approach data
structures describe the taxpayer and tax tables, and a program in which there are statements
representing an expert tax consultant’s knowledge, such as statements which relate information
about the taxpayer to tax table choices. It is this representation of the tax expert’s knowledge that
is difficult for the tax expert to understand or modify. In the expert system approach, the
information about taxpayers and tax computations is again found in data structures, but now the
knowledge describing the relationships between them is encoded in data structures as well. The
programs of an expert system are independent of the problem domain (taxes) and serve to
process the data structures without regard to the nature of the problem area they describe. For
example, there are programs to acquire the described data values through user interaction,
programs to represent and process special organizations of description, and programs to process
the declarations that represent semantic relationships within the problem domain and an
algorithm to control the processing sequence and focus.
The general architecture of an expert system involves two principal components: a problem
dependent set of data declarations called the knowledge base or rule base, and a problem
independent (although highly data structure dependent) program which is called the inference
engine.

Individuals Involved with Expert Systems

There are generally three individuals having an interaction with expert systems. Primary among
these is the end-user; the individual who uses the system for its problem solving assistance. In
the building and maintenance of the system there are two other roles: the problem domain
expert who builds and supplies the knowledge base providing the domain expertise, and a
knowledge engineer who assists the experts in determining the representation of their
knowledge, enters this knowledge into an explanation module and who defines the inference
technique required to obtain useful problem solving activity. Usually, the knowledge engineer
will represent the problem solving activity in the form of rules which is referred to as a
rule-based expert system. When these rules are created from the domain expertise, the knowledge
base stores the rules of the expert system.

Inference Rule

An understanding of the “inference rule” concept is important to understand expert systems. An
inference rule is a statement that has two parts, an if-clause and a then-clause. This rule is what
gives expert systems the ability to find solutions to diagnostic and prescriptive problems.

Example: An example of an inference rule is: If the restaurant choice includes French,
and the occasion is romantic, then the restaurant choice is definitely Paul Bocuse.
An expert system’s rulebase is made up of many such inference rules. They are entered as
separate rules and it is the inference engine that uses them together to draw conclusions. Because
each rule is a unit, rules may be deleted or added without affecting other rules (though it should
affect which conclusions are reached). One advantage of inference rules over traditional
programming is that inference rules use reasoning which more closely resemble human

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