Fuzzy Logic deals with those imprecise conditions about which a true/false value cannot be determined. Much of this has to do with the vagueness and ambiguity that can be found in everyday life. For example, the question: Is it HOT outside? probably would lead to a variety of responses from those asked. These are often labeled as subjective responses, where no one answer is exact. Subjective responses are relative to an individual's experience and knowledge. Human beings are able to exert this higher level of abstraction during the thought process.
For this reason, Fuzzy Logic has been compared to the human decision making process. Conventional Logic (and computing systems for that matter) are by nature related to the Boolean Conditions (true/false). What Fuzzy Logic attempts to encompass is that area where a partial truth can be established, that is a gradient within the true/false realm. In fuzzy set theory, although it is still possible to have an exact yes/no answer as to set membership, elements can now be partial members in a set.
Whereas other logic systems and foundations center on the quantitative aspect of an object, fuzzy logic describes the qualitative nature of things. In many ways, this concept of defining a fuzzy event or thing is related to the rules of grammar that focus on descriptive adjectives and adverbs. For example:
That dog barks loudly.
It is very cold outside.
In everyday life, there is no predefined set of decibel levels to determine just how loudly the dog is barking. Likewise for the temperature and weather declaration. Fuzzy logic attempts to take these rather variable statements and develop a method to establish the set with a relative degree of belonging.
Throughout history, even back to the days of Aristotle, true/false relationships have been the primary focus in logic development. The idea of multi-value logic had been explored to some degree, though not with the formality and description of other areas of logic.
The concept of Fuzzy Logic was introduced by Professor Lotfi A. Zadeh at the University of California at Berkeley in the 1960's. His goal was to develop a model that could more closely describe the natural language process
His pursuit in this field defined some of the basic terminology associated with fuzzy logic such as:
Fuzzy Set Theory, Fuzzification, Fuzzy Quantification and Fuzzy Events
Dr. Zadeh, as the prinicpal founder of the fuzzy logic theory has earned numerous Awards, Fellowships and Honors and has contributed a large amount of research and publications to the field of knowledge representation.
This trend towards representing the possible variations found in many everyday events has had its applications extend through various business, research, and system development circles, for example, its use in embedded control systems.
Specific benefits that have already been identified with the use of fuzzy logic include: reduced development time, quality improvement, ability to deal more efficiently and effectively with real time problems.
There is still an inherent difference between fuzzy set theory and the mathematical representation of Probability.
This timeline of the development and uses of fuzzy logic is just a basic overview of this still developing topic of research. With the expansive growth of Computer Systems and Artificial Intelligence, the need to further refine and apply this knowledge is also of great importance and magnitude.