2 cissp ® Official Study Guide Eighth Edition

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Expert Systems
Expert systems seek to embody the accumulated knowledge of experts on a particular sub-
ject and apply it in a consistent fashion to future decisions. Several studies have shown that 
expert systems, when properly developed and implemented, often make better decisions 
than some of their human counterparts when faced with routine decisions.
Every expert system has two main components: the knowledge base and the inference 
engine.
The knowledge base contains the rules known by an expert system. The knowledge base 
seeks to codify the knowledge of human experts in a series of “if/then” statements. Let’s 
consider a simple expert system designed to help homeowners decide whether they should 
evacuate an area when a hurricane threatens. The knowledge base might contain the fol-
lowing statements (these statements are for example only):
■
If the hurricane is a Category 4 storm or higher, then flood waters normally reach a 
height of 20 feet above sea level.
■
If the hurricane has winds in excess of 120 miles per hour (mph), then wood-frame 
structures will be destroyed.
■
If it is late in the hurricane season, then hurricanes tend to get stronger as they 
approach the coast.
In an actual expert system, the knowledge base would contain hundreds or thousands of 
assertions such as those just listed.
The second major component of an expert system—the inference engine—analyzes 
information in the knowledge base to arrive at the appropriate decision. The expert system 
user employs some sort of user interface to provide the inference engine with details about 
the current situation, and the inference engine uses a combination of logical reasoning and 
fuzzy logic techniques to draw a conclusion based on past experience. Continuing with the 
hurricane example, a user might inform the expert system that a Category 4 hurricane is 
approaching the coast with wind speeds averaging 140 mph. The inference engine would 
then analyze information in the knowledge base and make an evacuation recommendation 
based on that past knowledge.
Expert systems are not infallible—they’re only as good as the data in the knowledge 
base and the decision-making algorithms implemented in the inference engine. However, 
they have one major advantage in stressful situations—their decisions do not involve judg-
ment clouded by emotion. Expert systems can play an important role in analyzing emer-
gency events, stock trading, and other scenarios in which emotional investment sometimes 
gets in the way of a logical decision. For this reason, many lending institutions now use 
expert systems to make credit decisions instead of relying on loan officers who might say to 
themselves, “Well, Jim hasn’t paid his bills on time, but he seems like a perfectly nice guy.”

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