Expert System Architecture
Expert system consist of two primary parts:
- the knowledge base
- the inference [reasoning] engine
The knowledge base of expert systems contains both factual and
Factual knowledge is that knowledge of the task
domain that is widely shared, typically found in textbooks or journals,
and commonly agreed upon by those knowledgeable in the particular
Heuristic knowledge is more experiential, more
judgmental knowledge of performance, and is largely individualistic.
Heuristic knowledge is refered to in human terms as good judgment.
An expert system architecture is logically driven, rather than
physically. Knowledge is represented in expert systems in terms
of rules and/or units.
Rule Based Systems
Expert systems whose knowledge is represented in rule form are
called rule-based systems. A rule uses an IF statement to reach
a THEN conclusion.
Unit Based Systems
Another widely used representation of knowledge is the unit [also
known as frame, schema, or list structure]. Units are based upon
a passive view of knowledge, where a knowledge entity is broken
into associated symbols. Typically, a unit consists of a list of
properties of the entity and associated values for those properties.
A task domain has many entities that each have various relations.
These relations are also specified and linked. Sometimes, a unit
can also represent knowledge that is a "special case"
of another unit, or some units can be "parts of" another
The sequence of steps to reach a conclusion is not explicitly programmed
when the system is built; rather it is dynamically synthesized with
each new user case.
Expert systems can process multiple values for any problem parameter.
This permits more than one line of reasoning to be pursued and the
results of incomplete reasoning to be presented.
Problem solving is accomplished by applying specific knowledge
rather than specific technique. That is to say - the same expert
system procedures can be applied across various expert knowledge
The rulebase and inference engine cooperate to simulate the reasoning
process that a human expert pursues in analyzing a problem, and
arriving at a conclusion. To simulate the human reasoning process,
a vast amount of knowledge needed to be stored in the knowledge
base using "if then" type of statements.
Knowledge that is represented in the system appears in the rulebase.
In cross-referenced applications, there are basically four different
types of objects, with associated information present.
Classes - questions asked to the user.
Parameters - a place holder for a character string
which may be a variable that can be inserted into a class question
at the point in the question where the parameter is positioned.
Procedures - definitions of calls to external
Rule Nodes - the inferencing in the system is
done by a tree structure which indicates the rules or logic which
mimics human reasoning. The nodes of these trees are called rule
nodes. There are several different types of rule nodes.
Storage & Processing Capacity
Storing the rules base and related programs requires large storage
capacities. With personal storage capacity on PC's at such high
levels, it is now possible to run some types of simple expert systems
on personal computers. Although, with the trend to virtualization
- I expect the trend will move back to centralized processing.
An expert system may be required to diagnose a data processing
system comprising many separate components, some of which are optional.
By employing a single integrated rulebase to diagnose only a minimal
part of a data processing system, much of the rulebase is not required.
System rules can help segment and isolate the sections of rules
based required, and speed up the processing time.
Segmenting of the rulebase also allows the expert system to be
run with systems or on systems having much smaller memory capacities.
Each segment of the rulebase can be paged into and out of the system
The segmenting of the rulebase into contextual segments requires
that the expert system manage various intersegment relationships
as segments are paged into and out of memory during execution of
Since the system permits a rulebase segment to be called and executed
at any time during the processing of the first rulebase, provision
must be made to store the data that has been accumulated up to that
point so that at some time later in the process, when the system
returns to the first segment, it can proceed from the last point
or rule node that was processed.
Provision must also be made for data collected by the system up
to a certain point to be passed to the second segment of the rulebase,
after it has been paged into the system, and data collected during
the processing of the second segment can be passed to the first
segment when the system returns to complete processing that segment.
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