A fuzzy expert system is a form of artificial intelligence that uses a collection of membership functions (fuzzy logic) and rules (instead of Boolean logic) to reason about data. The rules in a fuzzy expert system are usually of a form similar to this: If x is low and y is high, then z = medium, where x and y are input variables (names for known data values), z is an output variable (a name for a data value to be computed), low is a membership function (fuzzy subset) defined on x, high is a membership function defined on y, and medium is a membership function defined on z. The antecedent (the rule's premise) describes to what degree the rule applies, while the conclusion (the rule's consequent) assigns a membership function to each of one or more output variables.
Most tools for working with fuzzy expert systems allow more than one conclusion per rule. The set of rules in a fuzzy expert system is known as the "rulebase" (or knowledge base).