In our daily lives, we can’t always pick between a Yes and a No. This occurs because you may encounter situations wherein you lack the necessary information to make a decision. Alternatively, you may be perplexed yourself.

For example, if someone asks whether you will be there on a certain day next month, you will probably not react instantly with Yes or No. Because you cannot guarantee that you will be available on that day next month.

Not simple, is it?

The term fuzzy refers to something that is unclear. When a situation is unclear, the computer, like humans, might be unable to provide a True or False decision. 1 symbolizes True in Boolean Logic, whereas 0 represents False.

In contrast, fuzzy logic takes into consideration all of an issue’s ambiguities, where there could be additional alternative values beyond a binary True and False. This is hugely useful in artificial intelligence, which needs to be more intuitive, adaptive, and human-like than traditional machine operations. On the occasion of World Logic Day (January 14^th), let us break down this vital concept.

How Does Fuzzy Logic Work?

Fuzzy logic considers human cognition to be the most critical data form for drawing exact conclusions. This logic was developed in 1965 at the University of California, Berkeley, by Lotfi Zadeh, who coined the term “fuzzy.” He argued that traditional computer logic was incapable of handling unclear or imprecise information.

Similar to humans, computers are capable of integrating a broad range of values that exist between True and False. These may include Definitely yes, Perhaps yes, Can’t say, Perhaps no, as well as Definitely no.

Check out this simple example of fuzzy logic to understand how it works:

Problem question: Is it sunny outside today?

Boolean solution: Yes (1) or No (0).

In line with normal Boolean algebra, the algorithm will receive a specified input and provide either Yes or No as its result. This is represented by 1 and 0 correspondingly. However, when fuzzy logic is used, other possibilities emerge.

Fuzzy logic solution:

• Very sunny with rare clouds (0.95)
• Moderately sunny (0.75)
• Partly sunny and partly cloudy (0.5)
• A little sunny but mostly cloudy (0.3)
• Very cloudy with rare sunny periods (0.1)

Fuzzy logic enables a wider range of outcomes, including extremely, somewhat, and not at all, as seen in the figure. These integers from 0 and 1 show the range of outcomes that are possible.

An approach based on fuzzy logic employs all relevant data to solve an issue. It then generates the optimum decision based on the available inputs. In circumstances when a clear rationale cannot be provided, it provides an acceptable substitute.

Understanding the Technical Architecture of Fuzzy Logic

Since it’s World Logic Day, let us take a closer look at the technical architecture that makes up a fuzzy logic solution. It will comprise:

• The central module for fuzzification: It transforms the input, which consists of uncertain numbers, into numerical value fuzzy subsets that are logically separated according to the preset criteria set.
• Rules counter: It stores the IF-THEN-ELSE-YES-NO — i.e., the types of human-defined conditional rules.
• Intelligence module: It replicates human reasoning logic by creating a fuzzy inference using inputs from fuzzy modules and predetermined rules.
• Defuzzification module: It transforms the fuzzy output from the intelligence unit into a crisp value output.

Fuzzy logic is excellent for modeling complicated situations with unclear or skewed inputs (like AI challenges) due to its resemblance to human decision-making. Fuzzy logic programs are simpler to create than conventional logic programs and use lesser instructions, hence reducing the amount of memory required to execute AI systems.

The Role of Fuzzy Logic in Artificial Intelligence

Many complex organizational issues cannot be resolved with yes/no or black/white programming responses. In situations where responses are sometimes ambiguous, fuzzy logic is beneficial. Fuzzy logic manages imprecision or ambiguity by associating multiple metrics of propositional believability.

• Fuzzy logic and semantics: In its most basic form, decision-tree analysis is utilized to develop fuzzy logic. Consequently, it may serve as the basis for artificial intelligence (AI) systems constructed with rule-based conclusions. Both fuzzy logic and fuzzy semantics (for example, the words “sunny” and “slightly,” which are unquantifiable) are crucial to the programming of artificial intelligence systems.
• Notable applications: AI technologies and applications are still evolving across a range of sectors, despite the fact that fuzzy logic programming capabilities are increasing. IBM’s Watson is one of the most prominent AI systems using fuzzy logic or fuzzy semantics. In the banking sector, investment reports are generated using fuzzy logic, machine learning, and similar technological systems.
• Fuzzy logic and machine learning: Sometimes, fuzzy logic and machine learning are grouped together, however, they are not identical. Machine learning refers to computer systems that replicate human intellect by modifying algorithms to repeatedly solve difficult issues. Fuzzy logic is a set of rules or processes that may operate on imprecise data sets, but the algorithms must still be written by humans. Both fields may be used in artificial intelligence and the resolution of difficult issues.
• Examples of fuzzy logic: Fuzzy logic may help neural networks, data mining, case-based reasoning (CBR), and business rules. For instance, fuzzy logic may be used in CBR to dynamically group information into categories, hence enhancing performance by reducing susceptibility to noise and outliers. Fuzzy logic also enables professionals in business rules to compose more effective rules. Here’s an instance of a revised rule that makes use of fuzzy logic.

When the quantity of cross-border transactions is “large” (a phrase with ambiguous meaning) and the transaction takes place in the evening (another term with ambiguous semantics), the transfer may be suspect.

Is Fuzzy Logic the Same as Probability Theory?

Probability and fuzzy logic are both crucial concepts for artificial intelligence, but the former has more to do with predictive analytics. In other words, probability refers to the accuracy of a predictive inference made using AI-based data analysis.

Although the terms may seem equivalent, fuzzy logic or probability are not interchangeable. Fuzzy logic is a worldview with varying degrees of truthfulness. Probability focuses on notions and statements that are either true or false – ideas that may be either right or wrong. The likelihood of a claim is the level of faith in its validity.

The definitions of fuzzy logic and probability differentiate them from one another. Probability is tied to occurrences, not facts, because events either occur or do not occur. There is no room for ambiguity. Fuzzy logic, on the other hand, strives to grasp the essence of uncertainty. It relates mostly to the level of truth.

Probability theory can’t be used to reason with notions you cannot describe as entirely true or false.

What Else Can You Do with Fuzzy Logic?

Fuzzy logic has applications in most computing fields that have to do with data operations, which includes artificial intelligence as well as data mining.

Data mining, a subject that connects mathematics, machine learning, and computer science, is the process of discovering significant relationships in massive data sets. Fuzzy logic is a set of rules that can be applied to fuzzy data sets in order to reach logical conclusions. It is a useful technique for discovering relevant connections in this type of data, given that data mining often includes imprecise measurements.

Using fuzzy logic mathematics, analysts may produce automated buy and sell signals in some complex trading systems. These technologies aid investors in adjusting to a large variety of changeable market situations that have an effect on their holdings.

Areas like banking, market intelligence, research, etc., are being completely revolutionized by AI, which is why we’ve covered fuzzy logic in our World Logic Day special! You now have a mine of new innovations to explore in AI – like generative AI that can create art out of a few words or phrases – which has led to growing investments in AI and artificial intelligence ETFs.