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Unit 13: Expert Systems and its Architecture

then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be Notes
true, then it is added to the list of goals.

Example: Suppose a rulebase contains:
(1) If Fritz is green then Fritz is a frog.
(2) If Fritz is a frog then Fritz hops.

Suppose a goal is to conclude that Fritz hops. The rulebase would be searched and rule (2) would
be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is
a frog, so this “if” statement is added to the goal list. The rulebase is again searched and this time
rule (1) is selected because its then clause matches the new goal just added to the list. This time,
the if-clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because
the list of goals determines which rules are selected and used, this method is called goal driven.

Task Make distinction between backward chaining and forward chaining.

13.2.2 Certainty Factors

One advantage of expert systems over traditional methods of programming is that they allow
the use of “confidences” (or “certainty factors”). When a human reasons he does not always
conclude things with 100% confidence. He might say, “If Fritz is green, then he is probably a
frog” (after all, he might be a chameleon). This type of reasoning can be imitated by using
numeric values called confidences.

Example: If it is known that Fritz is green, it might be concluded with 0.85 confidence
that he is a frog; or, if it is known that he is a frog, it might be concluded with 0.95 confidence that
he hops. These numbers are similar in nature to probabilities, but they are not the same. They
are meant to imitate the confidences humans use in reasoning rather than to follow the
mathematical definitions used in calculating probabilities.

Self Assessment

Fill in the blanks:
3. ....................... chaining starts with the data available and uses the inference rules to conclude
more data until a desired goal is reached.
4. ....................... chaining starts with a list of goals and works backwards to see if there is data
which will allow it to conclude any of these goals.

13.3 Expert System Architecture

Figure 13.2 shows the most important modules that make up a rule-based expert system. The
user interacts with the system through a user interface which may use menus, natural language or
any other style of interaction). Then an inference engine is used to reason with both the expert
knowledge (extracted from our friendly expert) and data specific to the particular problem
being solved. The expert knowledge will typically be in the form of a set of IF-THEN rules. The
case specific data includes both data provided by the user and partial conclusions (along with

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