Characteristics of Fuzzy Logic in AI
According to George Boole, human thinking and decision are based on YES or NO reasoning or large 1 and logic 0. Accordingly, Boolean algebra was developed. Even expert systems founded this logic. But it has been argued that human thinking doesn’t always follow crisp YES or NO logic rather it is quite vague, uncertain, imprecise or fuzzy in nature.
According to Lofti Zadeh, a computer scientist at the University of California, Boolean logic had its roots in the theory of crisp sets, and fuzzy logic has its roots in the theory of fuzzy sets. This process of making a crisp quantity fuzzy is known as Fuzzification.
Characteristics of Fuzzy Logic:
1. It describes vague natural real-world concepts.
2. A fuzzy set admits the possibility of partial members with associated membership characteristic function values.
3. The degree of an object belonging to a fuzzy set is denoted by a membership value between 0 and 1.
4. A membership function associated with given fuzzy set maps input value to its appropriate membership value.
5. Uncertainties are represented with membership functions and then this function is manipulated in a method defined in fuzzy theory.