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Expert System Architecture

Expert system consist of two primary parts:

  1. the knowledge base
  2. the inference [reasoning] engine


Knowledge Base

The knowledge base of expert systems contains both factual and heuristic knowledge.

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 field.

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 unit.


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 domains.

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 procedures.

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.

    • Goal Node - The top node of the tree that contains the conclusion. Each tree has a different goal node.
    • Rule Nodes - The branches of the tree
    • Evidence Node, an external node, or a reference node - A leaf of the branch. In responding to a question presented by an evidence node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe."

      Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner.

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.

Rulesbase Segmentation

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 as needed.

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 the program.

Data Storage

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.

NEXT: Expert System Interfaces


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