Fuzzy Logic is defined as a many-valued logic form which may have truth values of variables in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we may come across a situation where we can’t decide whether the statement is true or false. At that time, fuzzy logic offers very valuable flexibility for reasoning.

Fuzzy logic algorithm helps to solve a problem after considering all available data. Then it takes the best possible decision for the given the input. The FL method imitates the way of decision making in a human which consider all the possibilities between digital values T and F. [1]

Figure 1. Fuzzy logic System in AI

Figure 1 shows basically, it can be implemented in systems with various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in artificial intelligence. [3]

Advantages of Fuzzy Logic Systems

1. Generally, in this system, we can take imprecise, distorted, noisy input information.
2. Also, these logics are easy to construct and understand.
3. Basically, it’s solution to complex problems. Such as medicine.
4. Also, we can relate math in concept within fuzzy logic. Also, these concepts are very simple.
5. Due to the flexibility of fuzzy logic, we can add and delete rules in FLS system. [3]

Fuzzy Logic in Artificial Intelligence

The fuzzy logic systems generate the logical output in reply to the uncertain, messy, deform, and incomplete fuzzy inputs.

The artificial intelligent fuzzy logic is a process of reasoning for a problem that looks like human reasoning. The approach will be the replica of the way the human will make the decision and should involve all prospects of the problem with the digital output of YES and NO.

The basic logic system is an n-valued logic system that applies the scale of the state of inputs and in response give outputs based on the state of input and degree of change of these states. (as TRUE or FALSE on the other hand, for human understanding the output are YES or NO or partially YES or NO). Thus the inventor of the fuzzy logic systems has included all possible logic of inputs in the system to generate the definite output. [2]

Fuzzy Logic in AI: Example

The design of a fuzzy logic system starts with a set of membership functions for each input and a set for each output. A set of rules is then applied to the membership functions to yield a crisp output value. Let’s take an example of process control and understand fuzzy logic. [4]

References:

1. https://www.guru99.com/what-is-fuzzy-logic.html
2. https://www.softwaretestinghelp.com/fuzzy-logic-robotics-in-ai/
3. https://data-flair.training/blogs/fuzzy-logic-systems/
4. https://www.edureka.co/blog/fuzzy-logic-ai/

Cite this article:

Thanusri swetha J (2021), Fuzzy logic System in AI, Anatechmaz, pp. 47