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Knowledge Discovery Subject Enrichment

One of the most famous examples of an Expert System is Deep Blue, the IBM-developed, chess-playing
AI that defeated the human world chess champion in 1997. Deep Blue was developed in cooperation
between IBM’s software engineers and several chess grandmasters, who helped translate their human
chess expertise into tens of thousands of computer code rules for playing grandmaster-level chess.

2. Fuzzy Logic System

In the expert system described earlier, each variable or condition is either True or False. For it to work,
the system needs to know the absolute answer to questions such as whether or not the patient has a
fever. This could be reduced to a simple calculation of a temperature reading above 37 °C, but in reality,
the situation may not always be so clear.
Fuzzy logic is another approach to expert systems which allow variables to have a ‘truth value’ that is
anywhere between 0 and 1 and captures the extent to which it fits a category. This allows patients to
be assigned a rating of how well they fit the category of having fever. The figure might depend on the
patient’s temperature reading as well as other relevant factors, such as their age or the time of day, and
it allows the patient to be described as a borderline case.

Expert System Vs Fuzzy Logic System

Fuzzy logic is particularly useful for capturing intuitive knowledge, where experts make good decisions in
the face of wide-ranging and uncertain variables that interact with each other. They have been used to
develop control systems for cameras which automatically adjust their settings to suit the conditions, and
for stock trading applications to establish rules for buying and selling under different market conditions.
In each case, the fuzzy system continually assesses dozens of variables, follows rules designed by human
experts to adjust truth values and uses them to automatically make decisions.

Limitations of Symbolic AI Approach

Symbolic AI systems require human experts to encode their knowledge in a way the computer can
understand. This places significant constraints on their degree of autonomy. While they can perform
tasks automatically, they can only do so in the ways in which they are instructed, and they can only be

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