Initial idea 1: Fuzzy logic in AI

Fuzzy logic in AI

 

 

What is Fuzzy Logic?

(Electrical Technology, 2022)

 

Fuzzy logic is based on the theory of fuzzy sets, which is a generalisation of the classical set theory. Saying that the theory of fuzzy sets is a generalization of the classical set theory means that the latter is a special case of fuzzy sets theory. To make a metaphor in set theory speaking, the classical set theory is a subset of the theory of fuzzy sets (Zadeh, 2008).

Fuzzy logic is an extension of Boolean logic by Lotfi Zadeh in 1965 based on the mathematical theory of fuzzy sets, which is a generalization of the classical set theory. By introducing the notion of degree in the verification of a condition, thus enabling a condition to be in a state other than true or false, fuzzy logic provides a very valuable flexibility for reasoning, which makes it possible to consider inaccuracies and uncertainties. 

 

How is it applied in computing?

 

The main contribution of fuzzy logic is methodology for computing with words (CW). (Zadeh, 2022) No other methodology serves this purpose.

Computing with words is a methodology in which words are used in place of numbers for computing and reasoning. The point of this note is that fuzzy logic place a crucial role in CW and vice-versa. Thus, as an approximation, fuzzy logic may be equated to CW.

 

What are the benefits of fuzzy logic compared to other techniques?

 

The advantage of the fuzzy model is ability to transform the input indices SPI and CPI into linguistic variables, as well as linguistic evaluated overall project output. Using this approach, it is possible to simulate the risk and the uncertainty that are always associated with projects. (Doskéocil, 2022)

 

Are there any real life applications of fuzzy logic?

 

The applications of fuzzy logic, once thought to be an obscure mathematical curiosity, can be found in many engineering and scientific works. (Gordon, 2016) Fuzzy logic has been used in numerous applications such as facial pattern recognition, air conditioners, washing machines, vacuum cleaners, antiskid braking systems, transmission systems, control of subway systems and unmanned helicopters, knowledge-based systems for multiobjective optimisation of power systems, weather forecasting systems, models for new product pricing or project risk assessment, medical diagnosis and treatment plans, and stock trading. Fuzzy logic has been successfully used in numerous fields such as control systems engineering, image processing, power engineering, industrial automation, robotics, consumer electronics, and optimization. This branch of mathematics has instilled new life into scientific fields that have been dormant for a long time.

 

What are some of the recent advances in fuzzy logic?

 

Advances in Fuzzy Sets Extensions (FSE) (Edalatpanah et al., 2022) research have been essential to the Artificial Intelligence (AI) field, which has seen changing trends over the past few years. Many of the most recent applications of AI systems rely on their ability to solve problems by acquiring, representing, and processing expert knowledge. In order to make these tools more applicable, models should be developed that extract the maximum information from data. To do this, an approach is to imitate how humans think and represent the real world - and capture it within a mathematical language. It has been shown that fuzzy logic - a form of AI - and fuzzy set theory are useful in this situation. Many AI algorithms, heuristics, and methodologies are based on how the human brain solves problems, and FS represents expert knowledge in language expression. In this regard, today’s scientific and technological landscape supports FSE-based research.

 

 

Reference list:

  

Doskéocil, R., 2022. Fuzzy logic: An instrument for the evaluation of project status. [online] Econstor.eu. Available at: <https://www.econstor.eu/handle/10419/113883> [Accessed 20 April 2022].

 

Edalatpanah, S., Najafi, S., Kumar, R. and Mohapatra, H., 2022. Advances in Fuzzy Set Extensions: Theory, Models, and Applications. [online] Frontiers. Available at: <https://www.frontiersin.org/research-topics/33761/advances-in-fuzzy-set-extensions-theory-models-and-applications#overview> [Accessed 10 May 2022].

 

Electrical Technology, 2022. [image] Available at: <https://www.electricaltechnology.org/2018/02/fuzzy-logic-system.html> [Accessed 20 April 2022].

 

Gordon, A., 2016. Gaming Law Review and Economics, 20(10), pp.859-868.

 

Zadeh, L., 2022. Computing with Words in Information/Intelligent Systems 1.

 

Zadeh, L., 2008. Is there a need for fuzzy logic?. Information Sciences, 178(13), pp.2751-2779.