In our daily life, we cannot always choose between yes and no. This happens because you may encounter situations where you lack the necessary information to make a decision. Alternatively, you may be confused yourself.
For example, if someone asks if you’ll be there on a certain day next month, you probably won’t immediately respond with a yes or no. Because you cannot guarantee that you will be available on the same day next month.
Not easy, right?
The term fuzzy refers to something that is unclear. When a situation is unclear, the computer, like humans, may not provide a decision of right or wrong. 1 signifies True in Boolean logic, while 0 represents False.
In contrast, fuzzy logic takes into account all the ambiguity of an issue, where there can be additional alternative values beyond the binary value of true and false. This is very useful in artificial intelligence, which needs to be more intuitive, adaptive and human-like than traditional machine operations. On the occasion of World Reason Day (January 14God’), let’s break down this essential concept.
How does fuzzy logic work?
Fuzzy logic considers human cognition to be the most critical form of data for drawing accurate conclusions. This logic was developed in 1965 at the University of California, Berkeley, by Lotfi Zada, who coined the term “fuzzy”. He argued that traditional computer logic is unable to deal with unclear or imprecise information.
Like humans, computers are capable of combining a wide range of values that exist between true and false. These may include definitely yes, maybe yes, can’t say, maybe 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).
Following standard Boolean algebra, the algorithm will take a specified input and return a yes or no as a result. This is represented by 1 and 0 respectively. However, when using fuzzy logic, other possibilities emerge.
Fuzzy logic solution:
- Very sunny with rare clouds (0.95)
- moderate sun (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 allows for a wider range of outcomes, including extremes, somewhat, and not at all, as seen in the figure. These integers from 0 to 1 show the range of possible outcomes.
A fuzzy logic approach uses all relevant data to solve a problem. It then produces the optimal decision based on the available inputs. In circumstances where a clear rationale cannot be provided, it provides an acceptable substitute.
Understanding the technical architecture of fuzzy logic
Since this is World Logic Day, let’s take a closer look at the technical architecture that makes up a fuzzy logic solution. This will include:
- The central module for fuzzification: It transforms the input, consisting of uncertain numbers, into fuzzy subsets of numerical values that are logically separated according to predetermined criteria.
- Enumerates rules: It stores the IF-THEN-ELSE-YES-NO – that is, the human-defined conditional rule types.
- Intelligence module: It replicates the logic of human logic by generating fuzzy inferences using input from fuzzy modules and predetermined rules.
- Dissipation modulus: It turns the fuzzy output from the intelligence unit into a sharp value output.
Fuzzy logic is excellent for modeling complicated situations with uncertain or biased inputs (like artificial intelligence challenges) due to its similarity to human decision making. Fuzzy logic programs are simpler to create than conventional logic programs and use fewer instructions, hence reducing the amount of memory required to run artificial intelligence systems.
The role of fuzzy logic in artificial intelligence
Many complex organizational problems cannot be solved with yes/no or black/white programming answers. In situations where responses are sometimes ambiguous, fuzzy logic is helpful. Fuzzy logic manages imprecision or ambiguity by associating multiple measures of propositional credibility.
- Fuzzy logic and semantics: In its most basic form, decision tree analysis is used to develop fuzzy logic. As a result, it may serve as the basis for artificial intelligence (AI) systems built with rule-based inference. Both fuzzy logic and fuzzy semantics (for example, the words “sun” and “little”, which are not quantifiable) are essential for programming artificial intelligence systems.
- Notable applications: Artificial intelligence technologies and applications are still developing in a variety of sectors, despite the fact that fuzzy logic programming capabilities are increasing. IBM’s Watson is one of the most prominent artificial intelligence systems that use fuzzy logic or fuzzy semantics. In the banking sector, investment reports are produced using fuzzy logic, machine learning and similar technology systems.
- Fuzzy logic and machine learning: Sometimes, fuzzy logic and machine learning are grouped together, however, they are not the same. Machine learning refers to computer systems that replicate human intelligence by changing algorithms to repeatedly solve difficult problems. 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 solving difficult problems.
- Examples of fuzzy logic: Fuzzy logic may help neural networks, data mining, case-based reasoning (CBR) and business rules. For example, fuzzy logic may be used in CBR to dynamically group information into categories, thereby improving performance by reducing sensitivity to noise and outliers. Fuzzy logic also allows business law professionals to compile more efficient rules. Here is an example of a revised rule that makes use of fuzzy logic.
When the amount of cross-border transactions is “large” (an expression with ambiguous meaning) and the transaction is carried out 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 two essential concepts for artificial intelligence, but the former is more related to predictive analytics. In other words, probability refers to the accuracy of a predictive inference made using artificial intelligence-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 truth. Probability focuses on concepts and statements that are true or false—ideas that may be true or false. The likelihood of a claim is the level of belief in its validity.
The definitions of fuzzy logic and probability differentiate them from each other. Probability is related to occurrences, not facts, because events either occur or do not occur. There is no room for ambiguity. Fuzzy logic, on the other hand, aims to capture the essence of uncertainty. It mainly refers to the level of truth.
Probability theory cannot be used to reason with concepts that you cannot describe as absolutely true or false.
What else can you do with Fuzzy Logic?
Fuzzy logic has applications in most areas of computing related to data operations, including artificial intelligence as well as data mining.
Data mining, a subject that connects mathematics, machine learning and computer science, is a process of discovering meaningful relationships in massive data sets. Fuzzy logic is a set of rules that can be applied to fuzzy data sets to reach logical conclusions. This is a useful technique for discovering relevant relationships in this type of data, given that data mining often involves imprecise measurements.
Using fuzzy logic math, analysts may generate automated buy and sell signals in some complex trading systems. These technologies assist investors in adapting to a wide variety of changing market conditions that have an impact on their holdings.
Fields like banking, market intelligence, research, etc., are being completely revolutionized by AI, which is why we covered fuzzy logic in our World Logic Day special! You now have a mine of new innovations to explore in AI—like creative AI that can create art from a few words or phrases—which has led to increased investment in AI and AI ETFs.