Expert systems use advanced computer logic to make decisions based on a large database of information. They can use traditional Boolean logic or fuzzy logic to compute answers. Fuzzy expert systems are more human-like and use inference to draw conclusions.
Technology has always been about building better, faster, smarter cars. Expert systems embrace this concept by using advanced computer logic to create software that appears to “think” and make decisions on its own. Traditionally based on Boolean logic – logic that uses only true or false values – expert systems use complex algorithms to compute answers from a large database of information. If the computer cannot determine the correct answer, it is assumed not that the program is wrong, but that the knowledge base does not contain enough information on the subject.
When a computer has to make a decision, it all boils down to a series of true or false statements. If programmed to turn on when a button is pressed, pressing the button sets it to true and not pressing the button sets it to false. False means no light while true turns on the light. This is the basis of computer logic.
An expert system takes these true and false answers to a new level. By combining a series of true and false answers, the computer tries to determine how to react to a given situation. He can change his answer according to the specific pattern and the number of true and false answers.
The idea behind these systems is based on how people think. Humans can store large amounts of new knowledge and make decisions based on previous knowledge. The computer is programmed to “think” and make decisions based on the knowledge found in its database and its previous experiences. In a sense, it is as if the computer is “learning” from its past successes and failures.
There are two main forms of expert systems. The traditional expert system uses Boolean logic to make its decisions. A fuzzy logic expert system, on the other hand, doesn’t. Calculate a range of values that fall between simple true or false answers to determine how truer or more false a statement is.
Fuzzy expert systems are more human-like than traditional expert systems in the way they “think”. These expert systems are not given specific answers to a problem, but rather a statement from which they draw further conclusions. This process is known as inference.
For example, if a statement says “All cats are striped. Miss Kitty is a cat,” fuzzy expert systems would infer that since all cats are striped and Miss Kitty is a cat, then Miss Kitty must be striped. Fuzzy logic can also compute more complicated values, such as determining the probability that a specific female cat will be streaked if only a percentage of females have streaks. Traditional expert systems would need many more instructions to reach these same conclusions.
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