Fuzzy logic is a type of mathematics and programming that more accurately represents how the human brain categorizes objects, evaluates conditions, and processes decisions. In the traditional logic system, an item strictly does or does not belong to a group, called a set. That is, an animal either is or is not a dog. Fuzzy logic allows an object to belong to a set to a certain degree or with a certain confidence. Applications of fuzzy logic in contemporary computer systems are too numerous to cite, but they control things like heating mixtures and tooling parts.

Our world is incredibly complex, both in breadth and depth. In some ways, it is difficult to adhere to the logical constraints of traditional set theory when describing how we make simple, daily decisions, such as cooking a roast or driving with traffic. Yet we expect computers to make these decisions by simplifying or collapsing complexity and not taking into account uncertainty. Fuzzy logic was invented, and coined, by Dr. Lotfi Zadeh at UC Berkeley in 1965, when he was thinking about math, linguistics, and common sense.

To understand how fuzzy logic isn't a vague, tentative system, but can be used very practically to teach computers how to make decisions, lets look at an example. Perhaps your mother had a rule, "No dogs in the house." Logically, this means that IF the object is a dog, THEN it is not to be in the house. Somehow, we can deduce that a stuffed animal resembling a Dalmatian will be allowed in, but a Dalmatian with skin and bones won't. What about a seeing-eye dog? What about an animal that is half Husky and half wolf?

Fuzzy logic allows for these in-betweens when it comes to meeting requirements and initializing consequences. Instead of an animal absolutely belonging to the set of dogs, it can belong to a certain extent. A golden retriever might have an associated value of 1.0, as close to "completely" dog as possible, while a Chihuahua might have .8, due to its size, and a seeing-eye dog only a .4, since it is often allowed where other dogs do not go.

This flexible system solves problems and controls machines that a simplistic logic system could not. The output, or the decisions, is always clear and not fuzzy, known as "crisp." Eventually, the dog is either in the house or out on the porch. It's never halfway in. That's why "fuzzy" doesn't mean uncertain or unknown.