Fuzzy logic is a type of mathematical logic that deals with reasoning that is approximate rather than exact. It was developed by [[Lotfi Zadeh]] in the 1960s as a way to handle the inherent uncertainty and imprecision in human reasoning. Unlike classical logic, which operates on binary values (true or false), fuzzy logic allows for degrees of truth. In other words, instead of assigning a statement an absolute true or false value, fuzzy logic assigns it a value between 0 and 1, representing the degree of truthfulness. This allows for more nuanced and flexible reasoning. Fuzzy logic also differs from other logics in its handling of vagueness and ambiguity. While classical logic assumes clear-cut definitions and precise boundaries between categories, fuzzy logic acknowledges that many concepts are inherently fuzzy and can have degrees of membership to different categories. Another important aspect of fuzzy logic is its use of linguistic variables and fuzzy sets. Instead of using numerical values directly, fuzzy logic employs linguistic terms (such as "very hot" or "slightly cold") to describe variables. These terms are then mapped to fuzzy sets, which assign membership values based on the degree to which an element belongs to the set. Overall, fuzzy logic provides a framework for dealing with uncertain and imprecise information more effectively than traditional logics. It has found applications in various fields such as control systems, artificial intelligence, decision making, pattern recognition, and many more where human-like reasoning is required. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Fuzzy logic") ```