2. #
DEFINITIONS
•An expert system is a computer program conceived to simulate some forms of human
reasoning (by the intermediary of an inference engine) and capable to manage an
important quantity of specialized knowledge.
•A system that uses human knowledge captured in a computer to solve problems that
ordinarily require human expertise (Turban & Aronson, 2001).
•A computer program designed to model the problem solving ability of a human expert
(Durkin, 1994).
•An intelligent computer program that uses knowledge and inference procedures to
solve problems that was difficult enough to acquire significant human expertise for their
solutions (Feigenbaum).
3. #
OVERVIEW OF AN EXPERT SYSTEM
Can…
•Explain their reasoning or suggested decisions
•Display intelligent behavior
•Draw conclusions from complex relationships
•Provide portable knowledge
4. #
COMPONENTS OF AN EXPERT SYSTEM
•User interface – the mechanism by which the user and the expert system
communicate.
•Explanation facility – explains the reasoning of the system to a user.
•Working memory – a global database of facts used by the rules.
•Inference engine – makes inferences by deciding which rules are satisfied by facts or
objects, prioritizes the satisfied rules, and executes the rule with the highest priority.
•Agenda – a prioritized list of rules created by the inference engine, whose patterns are
satisfied by facts or objects in working memory.
•Knowledge acquisition facility – an automatic way for the user to enter knowledge in
the system instead of having the knowledge engineer explicitly code the knowledge.
6. #
The knowledge base is also called the production memory in a rule-based expert
system.
As a simple example, consider the problem of deciding to cross a street. The
productions for the two rules are as follows, where the arrows mean that the system will
perform the actions on the right of the arrow if the conditions on the left are true.
the light is red -> stop
the light is green -> go
7. #
The production rules can be expressed in an equivalent pseudocode IF... THEN
format as:
Rule: Red_light
IF
the light is red
THEN
stop
Rule: Green_light
IF
the light is green
THEN
go
8. #
•Two general methods of inference are commonly used as the problem-solving
strategies of expert systems: forward chaining and backward chaining.
•FORWARD CHAINING - reasoning from facts to the conclusions resulting from those
facts. For example, if you see that it is raining before leaving home (the fact) then you
should take an umbrella (the conclusion).
•BACKWARD CHAINING – It involves reasoning in reverse from a hypothesis, a
potential conclusion to be proved, to the facts that support the hypothesis. For example,
if you have not looked outside and someone enters with wet shoes and an umbrella,
your hypothesis is that it is raining.
•In order to support this hypothesis you could ask the person if it was, in fact, raining. If
the response is yes, then the hypothesis is proved true and becomes a fact.
9. #
•The working memory may contain facts regarding the current status of the traffic light
such as "the light is green" or "the light is red."
•Either or both of these facts may be in working memory at the same time.
• If the traffic light is working normally, only one fact will be in memory.
•As Expert Systems evolved many new techniques were incorporated into various types
of inference engines. Some of the most important of these were:
Truth Maintenance
Hypothetical Reasoning
Fuzzy Logic
Ontology Classification
10. #
PARTICIPANTS IN EXPERT SYSTEMS DEVELOPMENT AND USE
Domain expert
•The individual or group whose expertise and knowledge is
captured for use in an expert system
Knowledge user
•The individual or group who uses and benefits from the expert
system
Knowledge engineer
•Someone trained or experienced in the design, development,
implementation, and maintenance of an expert system
12. #
ADVANTAGES
Easy to develop and modify
The use of satisficing
The use of heuristics
Development by knowledge engineers and users
13. #
LIMITATIONS
• Not widely used or tested
• Limited to relatively narrow problems
• Cannot readily deal with “mixed” knowledge
• Possibility of error
• Cannot refine own knowledge base
• Difficult to maintain
• May have high development costs
• Raise legal and ethical concerns
