Fuzzy logic (FL) is a mathematical technique for dealing with imprecise data and problems that have many solutions rather than one. Although it is implemented in digital computers which ultimately make only yesno decisions, FL works with ranges of values, solving problems in a way that more resembles human logic. FL is a multi-valued (as opposed to binary) logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: 0 and 1, black and white, yes or no; in terms of Boolean algebra, everything is in one set or another but not in both. FL allows for partial membership in asset values between 0 and 1, shades of gray, and introduces the concept of the “fuzzy set.” When the approximate reasoning of FL (Zadeh, 1965) is used with an expert system, logical inferences can be drawn from imprecise relationships. FL theory was developed by Lofti A. Zadeh at the University of California in the mid 1960s. However, it was not applied commercially until 1987 when the Matsushita Industrial Electric Co. used it to automatically optimize the wash cycle of a washing machine by sensing the load size, fabric mix, and quantity of detergent and has applications in the control of passenger elevators, household applications, and so forth.