Making decisions is without a doubt one of the most basic human activities. Nowadays, when it comes to decision making, most of the time we hear Analytics, Machine learning, etc which are basically branches of Artificial Intelligence. In this article, we will take a look at another possible methodology to make decisions at a more minute level, that is Fuzzy logic systems. Below are the major points listed that are to be discussed in this article. 

Table of contents

1. What is Fuzzy logic
2. Features of fuzzy logic
3. How it can be used for decision-making
4. Comparison between ML and Fuzzy logic
5. Application of fuzzy logic in decision making

Let’s start the discussion by understanding what fuzzy logic is.

The classical logic block accepts exact input and outputs a definite result as TRUE or FALSE, which is comparable to YES or NO in human terms. Lotfi Zadeh, the developer of fuzzy logic, noted that, unlike computers, humans make decisions based on a range of options between YES and NO, such as, Certainly Yes or No, Possibly Yes or No, and Can not say.  

Take a look at the diagram below. It demonstrates that in fuzzy systems, values are represented by a number ranging from 0 to 1. Absolute truth is represented by 1.0, while absolute falsehood is represented by 0.0. The truth value is a number that indicates the value in a fuzzy system

To put it another way, fuzzy logic is a logic that is used to describe fuzziness rather than logic that is fuzzy. There are a plethora of other examples like this that can help us grasp the concept of fuzzy logic.

To achieve a definite output, fuzzy logic operates on the levels of input possibilities. It can be used in systems ranging in size and capability from small microcontrollers to large networked workstation-based control systems.

Features of fuzzy logic

It can help us organize our thoughts into clear and concise sentences. As a result, Fuzzy logic is a process for describing the human proclivity for accurate thinking, which is a generalization of classical logic. It is recognized as a type of multi-values logic derived from fuzzy set theory. 

It is determined by the relative degrees of association and is motivated by human understanding and cognition methods that are ambiguous, inaccurate, partially true, or necessitate sharp boundaries. The scientific perspective of Fuzzy Logic is to explain numerous issues of approximate representation or indefinite data for providing the expected means to employ information and human expertise.

How it can be used for decision making

Decision-making is, as the name implies, the study of how decisions are made and how they can be made better or more successfully. In other words, the field is concerned with both descriptive and normative theories.

Much of the emphasis in developing the field has been on management, where the decision-making process is critical for functions such as inventory control, investment, personnel actions, new product development, and resource allocation, among many others. 

However, decision-making is broadly defined to include any choice or selection of alternatives and is thus important in many fields, including both the “soft” social sciences and the “hard” disciplines of natural sciences and engineering.

Fuzzy logic begins with and expands on a set of human language rules supplied by the user. These rules are converted to mathematical equivalents by fuzzy systems. This simplifies both the system designer’s and the computer’s jobs while producing far more accurate representations of how systems behave in the real world. 

Another advantage of fuzzy logic is its adaptability and ease of use. Fuzzy logic can solve problems with imprecise or incomplete data and model nonlinear functions of any complexity. Fuzzy will produce a better solution than traditional control techniques.

Any set of input-output data can be matched with a fuzzy system. Adaptive techniques like adaptive neuro-fuzzy inference systems (ANFIS) and fuzzy subtractive clustering are available in the Fuzzy Logic Toolbox, making this particularly simple.

A set of conditional “if-then” rules make up fuzzy logic models, often known as fuzzy inference systems. These rules are simple to write for a designer who understands the system, and as many rules, as are required to accurately explain the system can be provided (although typically only a moderate number of rules are needed).

The steps involved in fuzzy decision-making are as follows, as shown in the diagram above.

1. The identification of variables and alternatives is the first step.
2. The linguistic variables are converted to real variables during the fuzzification process.
3. The user selects the variables that must be included in the knowledge base.
4. The membership function is a mathematical expression of the membership function.
5. The if-then condition rule will be given next. Each variable corresponds to a single rule.
6. The next step is to convert the fuzzy value into an output variable.
7. The final stage of the fuzzy process is the actual implementation of the alternative. If the implementation is successful, it will improve the system’s operation in relation to the process’s goal.

Comparison between ML and Fuzzy logic

Machine learning algorithms are built to extract information from massive volumes of data while also giving conventional classification and clustering methods. It can handle a large range of data and be applied in a variety of settings.

In addition, learning time is needed for algorithms to improve in terms of accuracy and relevance. Fuzzy logic assesses a scenario’s confidence, and the algorithms are robust and respond swiftly to changing circumstances.

Fuzzy Logic is one of the Artificial Intelligence approaches/techniques for achieving intelligent behaviour by creating fuzzy classes of some parameters. The benefit, in this case, is that the rules and criteria are human-friendly. A domain expert typically defines these rules and fuzzy classes. As a result, fuzzy logic necessitates a significant amount of human intervention.

Application of fuzzy logic in decision making

Fuzzification of classical decision-making theories was used to apply fuzzy sets in decision-making for the most part. Probabilistic decision theories and game theories have both been used to model decision-making in risky situations. Fuzzy decision theories attempt to address the ambiguity and non-specificity that characterizes human preference, constraint, and goal formulation.

Some of the commonly known applications can be summarized as,

• Fuzzy Logic is used with Neural Networks because it simulates how people make decisions, but much faster. It is accomplished by aggregating data and transforming it into more meaningful data using partial truths in the form of fuzzy sets.
• In the large company business, it is used for decision-making support systems and personal evaluation.
• It has been used in automotive systems to control speed and traffic.
• Fuzzy logic is used in Natural Language Processing and a variety of Artificial Intelligence applications.
• It’s used in the aerospace industry to control the altitude of spacecraft and satellites.
• Modern control systems, such as expert systems, make extensive use of fuzzy logic.

Final words

Through this article, we have discussed how the Fuzzy logic system is used for decision-making. In support of that, from the start, we tried to understand the system, its features, and more importantly how it can be leveraged in decision making. Proceeding with this, we discussed the comparison between ML and the Fuzzy system. Lastly discussed its important applications. 

References 

• Machine Learning And Fuzzy Logic For Better Decision Making
• Application Of Fuzzy Logic In Decision Making