Fuzzy expert systems are a type of decision-making computer software that uses fuzzy logic, which allows for answers that are not just yes or no. The concept was introduced by Dr. Lotfi Zadeh in the 1960s and is based on mathematical values assigned to terms. Knowledge bases are crucial to fuzzy expert systems, and if the answer is incorrect, it means that the knowledge base needs more information.
The fuzzy expert system is a form of problem solving used by a computer system, often used in the creation of artificial intelligence. Expert systems are types of decision-making computer software based on Boolean logic, which means that the system uses a series of yes-or-no answers to try to solve a problem. Fuzzy expert systems expand on the traditional expert system and are based on fuzzy logic rather than Boolean logic. Fuzzy logic, as the name suggests, means that the answer isn’t a clear yes or no. It falls somewhere in between, and the computer has to try to calculate an answer based on answers that may not be entirely true but may not be entirely false either.
Known as the “father of fuzzy logic,” Dr. Lotfi Zadeh introduced the concept of fuzzy logic in the 1960s while employed at the University of California at Berkeley. He published an article in 1965 about blurry sets. He explained not only the idea of fuzzy sets and logic, but also a framework for incorporating this new logic into the world of engineering. He also coined the term “fuzzy,” in reference to this particular logic style, and the name stuck.
To understand the theory behind fuzzy expert systems, you need to understand the basic concepts of Boolean logic and fuzzy logic. While both are based on advanced mathematical algorithms, the basic concept is simple. Both use the answers to a series of questions or statements to formulate a new answer. In Boolean logic, answers are either true or false, while in fuzzy logic an answer can be true, partially true, false, partially false, and several values in between, depending on which terms the programmer puts into the program.
For example, if an expert system wanted to make a decision using Boolean logic, it would ultimately answer true or false, also called yes or no. An expert system using fuzzy logic, however, might answer yes, no, maybe, or some other combination. It does this by drawing conclusions from its current knowledge base of information.
Knowledge bases are at the heart of fuzzy expert systems. If a computer fails to give the correct answer, it is assumed that the knowledge base does not contain enough information rather than assuming that the program itself is wrong. The knowledge base might contain a statement such as “When x=yes and y=no then z=maybe”. From this statement, fuzzy expert systems can conclude that when “x=yes” and “y=yes” that “z” must also equal “yes”, or that when “x=no” and “y=yes” that “z” ” is still equal to “maybe”. If this is not the answer the programmer wanted, it means that the knowledge base needs more information to provide the correct answer.
Fuzzy expert systems make these calculations based on mathematical values. “Yes”, “no” and “maybe” are assigned certain values. The computer looks at what the values of the terms are in statements like “x=yes and y=no” and adds their values. Then add any other relevant values and match the final value with an answer like “maybe”, “yes” or “no”. So adding the mathematical values of “x=no” and “y=yes” tells the computer that the mathematical value of “z” equals “maybe”.
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