Fuzzy Logic

Fuzzy logic is a branch of mathematical logic that deals with reasoning that is approximate rather than exact and precise. It attempts to explain how humans make decisions when faced with multiple and often conflicting sources of information and how they interpret vague information such as natural language. Fuzzy logic is important for artificial intelligence and its components because it can comprise data from different sources. Traditional Boolean logic only deals with true (1) and false (0) values in order to make decisions, but fuzzy logic has more variables and can produce results that are between true and false, such as "maybe" or "somewhat true." Because of this, fuzzy logic is useful when data is fuzzy, uncertain, or unfeasible to measure precisely. In a practical sense, fuzzy logic can help in managing uncertainty and increasing the speed of more complex computational decisions. That is, by making connections between data from multiple sources, a fuzzy logic system can take preliminary decisions quickly and then search databases for more specific information. This can save time and increase accuracy. Fuzzy logic is also useful for robotics, self-learning systems, and decision making. It enables a robotic system to dynamically respond to its environment and make decisions on its own. Fuzzy logic can be useful in data mining, pattern recognition, finance, control, optimization, navigation, manufacturing, and other areas as well. Fuzzy logic can be used to create fuzzy sets—collections of values, and it can manipulate these sets in order to make decisions. In other words, it uses mathematics to calculate uncertainty. This is beneficial when more traditional logic cannot provide a certain answer.