Fuzzy Logic
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
A form of logic that reasons with degrees of truth between 0 and 1 instead of strict true or false, built for vague concepts like 'warm' or 'tall'.
What is Fuzzy Logic?
Fuzzy logic is a form of logic that reasons with degrees of truth between 0 and 1, rather than the strict true-or-false of classical logic. It exists because much of what people say about the world is vague: a room is somewhat warm, a driver is fairly cautious.
Lotfi Zadeh introduced the underlying theory of fuzzy sets in 1965. Note the distinction from probability: a 0.7 membership in “warm” does not mean a 70% chance the room is warm. It means the room is warm to degree 0.7.
How Fuzzy Logic Works
A fuzzy system runs in three stages. First, fuzzification: crisp inputs are mapped onto fuzzy sets through membership functions, so a reading of 24°C might belong to “warm” at 0.6 and “hot” at 0.2.
Second, rule evaluation. The system holds if-then rules written in those vague terms (“if warm, run the fan at medium speed”), and each rule fires in proportion to how true its condition is. The warm rule above fires at strength 0.6, the hot rule at 0.2, and both contribute.
Third, defuzzification: the fired rules are combined back into one crisp output, such as a fan speed of 55%. The vagueness lives inside the reasoning; the action that comes out is exact.
Applications of Fuzzy Logic
- Appliance and vehicle controllers: Washing machines that adjust cycles to how dirty the load is, air conditioners that modulate cooling, and braking systems that apply force gradually all encode expert judgment as fuzzy rules.
- Medical decision support: Symptoms arrive in degrees (“moderate pain”, “slightly elevated”), and fuzzy rules reason over them without forcing false precision.
Example of Fuzzy Logic
A conventional thermostat is binary: below the setpoint the cooling is on, above it, off. The result is a sawtooth of overshoot and abrupt switching.
A fuzzy air conditioner reads the same sensor differently. At 24°C, “warm” is true at 0.6 and “hot” at 0.2, so the rule for medium cooling fires strongly and the rule for full cooling fires weakly.
Defuzzification blends them into a single command, roughly half fan speed, and the blend shifts smoothly as the temperature drifts. The machine behaves the way a person adjusting a dial would, because its rules were written in a person’s terms.
Related AI terms: Heuristic · Expert System · Knowledge Representation · Algorithm
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