Information Technology Reference
In-Depth Information
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Develop a solution faster than human experts can
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Provide expertise needed for training and development to share the wisdom and expe-
rience of human experts with many people
Components of Expert Systems
An expert system consists of a collection of integrated and related components, including a
knowledge base, an inference engine, an explanation facility, a knowledge base acquisition
facility, and a user interface. A diagram of a typical expert system is shown in Figure 11.8.
In this figure, the user interacts with the interface, which interacts with the inference engine.
The inference engine interacts with the other expert system components. These components
must work together to provide expertise. This figure shows the inference engine coordinating
the flow of knowledge to other components of the expert system. Note that different knowl-
edge flows can exist, depending on what the expert system is doing and the specific expert
system involved.
Figure 11.8
Explanation
facility
Inference
engine
Components of an Expert
System
Knowledge
base
acquisition
facility
User
interface
Knowledge
base
Experts
User
The Knowledge Base
The
knowledge base
stores all relevant information, data, rules, cases, and relationships that
the expert system uses. As shown in Figure 11.9, a knowledge base is a natural extension of
a database (presented in Chapter 5) and an information and decision support system
(presented in Chapter 10). A knowledge base must be developed for each unique application.
For example, a medical expert system contains facts about diseases and symptoms. The
following are some tools and techniques that can be used to create a knowledge base.
knowledge base
A component of an expert system
that stores all relevant information,
data, rules, cases, and relationships
used by the expert system.
Figure 11.9
Database
raw
facts
Knowledge base
patterns and
relationships
Information and decision support
information
The Relationships Among Data,
Information, and Knowledge
Increasing understanding
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Assembling human experts.
One challenge in developing a knowledge base is to assemble
the knowledge of multiple human experts. Typically, the objective in building a
knowledge base is to integrate the knowledge of people with similar expertise (for
example, many doctors might contribute to a medical diagnostics knowledge base).
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Using fuzzy logic.
Another challenge for designers and developers of expert systems is
capturing knowledge and relationships that are not precise or exact. Instead of the black-
and-white, yes/no, or true/false conditions of typical computer decisions, fuzzy logic
