Insight into the concept of Fuzzy Logic in Artificial Intelligence
In actuality, there exists much fuzzy knowledge which is uncertain or probabilistic of its nature. Especially human thinking is more associated with the fuzzy information. Humans can give acceptable answers, which are probably correct whereas our system at times lacks the similar ability as they are based upon classical set theory and two valued logic which only accepts "True" or "False". To enable our systems to give complete information rather than just accepting the value of 0 and 1 and give opinions. Fuzzy sets have been able to give solutions to many real world problems.
Fuzzy logic is an approach that is similar to human reasoning. Its determination is based on the degrees of truth factor rather than the usual yes or no i.e. Boolean logic. It is based on the levels of probabilities of input to achieve the definite output. It is a form of artificial intelligence software and hence can be considered as a subset of AI
It can be implemented in software, hardware or combination of both with various sizes and capabilities. The basic architecture of Fuzzy logic consists of four parts:

Fuzzification Module – This basically deals with the transformation of input and fitting it into fuzzy sets.

Knowledge Base − It stores IFTHEN rules provided by experts.

Inference Engine − It accepts and promotes human interpretation by making fuzzy inference according to inputs and IFTHEN rules.

Defuzzification Module − It provides a certain crisp value corresponding to the fuzzy set obtained by the inference engine. The result obtained from this module is usually a physical quantity admissible by the real system.
A number of other concepts are associated with fuzzy logic such as fuzzy set theory, fuzzy modelling, the fuzzy control system that have been developed for further enhancement. In control systems theory, if the fuzzy interpretation of the problem is appropriate and if the fuzzy theory is developed precise and correct, then fuzzy controllers can be accordingly designed and they work quite well to their advantages. Most of the fuzzy logic control systems are knowledgebased systems which mean either their fuzzy models or their fuzzy logic controllers are described by fuzzy logic IFTHEN rules.
Fuzzy logic has a great many advantages:

Mathematical concepts within fuzzy reasoning are very simple and accept imprecise data.

You can modify a Fuzzy logic system because of its flexibility of fuzzy logic.

FLS is based on natural language and is easy to construct and understand.

It provides us with solutions to a number of complex problems in all fields.

There are certain disadvantages associated with the fuzzy concepts.

There is no systematic and welldefined strict approach to fuzzy system designing.

They are most suitable for the problems possessing low accuracy and understandable only when simple.
There are a wide variety of applications corresponding to fuzzy logic. Key application areas include Domestic Goods, Consumer Electronic Goods, Environment Control and much more.
The extract of systems control is to achieve and move towards automation. To achieve this motive fuzzy control technology and advanced computer technologies are integrated together to provide an approach that can imitate human thinking. This leads to a control system with a certain degree of AI. The concept provides a great help in dealing with the most complex situations and has been accepted by modern technology leading towards the transformation of systems with great potential.
A Fuzzy World  Explaining Fuzzy Logic
Video & Image Source: Kyle Flenar, Mathworks