6 Branches of Artificial Intelligence Explained

Branches of artificial intelligence are machine learning, expert systems, neural networks, natural language processing, robotics, and fuzzy logic.

Artificial Intelligence is intelligence possessed by  computers, which is designed to bear similarities to human intelligence.

This article discusses the branches of artificial intelligence, according to the following outline;

-Factors Used to Differentiate the Various Branches of Artificial Intelligence

-Branches of Artificial Intelligence





Factors Used to Differentiate the Various Branches of Artificial Intelligence

The main factor used to differentiate the various branches of artificial intelligence is the mode of application.

Each branch of artificial intelligence is unique with regards to the manner in which it is applied.

Other factors that differentiate the branches of artificial intelligence are complexity, mode of operation, and utilization.


Branches of Artificial Intelligence

1). Machine Learning as one of the Branches of Artificial Intelligence

Machine learning is a branch of artificial intelligence that involves developing and applying algorithms that enable a computer software to perform tasks through an adaptive process.

In machine learning, it is not necessary for the system to be programmed to perform a specific function [8]. The aim of machine learning is to provide solutions to real-life problems by processing, analyzing, and interpreting data.

Machine learning is an advanced aspect of artificial intelligence, that allows machines to learn, and execute functions independently by learning from past events.

There are three subcategories of machine learning, based on the design and function. These are; supervised, unsupervised and reinforcement machine learning [7].

Supervised Learning is a type of machine learning whereby the input and output data are both specified or labelled.

In supervised machine learning, labeled training data is fed into the system, to define specific algorithms for analyzing data and making predictions [3].

Unsupervised Learning is a type of machine learning where unlabeled or unspecified data is used to train the system.

This means that, in unsupervised learning, the datasets which the algorithm analyses, are not specified or assigned any expected outcomes.

Artificial intelligence systems that use unspecified machine learning, develop their algorithms based on association, dimensionality reduction, and clustering.

By functioning according to the mechanisms stated above, unsupervised learning models do not need any supervision from the user.

Reinforcement Learning is a type of machine learning that works based on making decisions between negative and positive signals [2].

This type of reinforcement learning functions based on a repetitive mechanism of trial and error interactions [6].

In reinforcement learning, neural networks are often used to solve complex problems, by identifying links between datasets.


2). Expert Systems

An expert system is an artificial intelligence system which makes decisions and solves complex problems using heuristics and stored data [5].

Expert systems are knowledge-based; meaning that they typically employ stored data to solve problems.

With regards to decision-making, expert systems are generally designed to imitate human cognitive behaviors, in terms of correlating datasets and identifying similarities and repetitive patterns.

The mechanism by which expert systems work, is a result-oriented mechanism. These systems do not rely on conventional programming methods, but rather on the delivery of a suitable outcome, in response to an input.


3). Neural Networks

Neural networks are artificial intelligence systems that are designed after human neurological systems, and are used to solve real-world problems by identifying relational links between variables in datasets.

With neural networks, a structural approach is utilized to classify data and draw correlations effectively.

Areas of the application of this type of artificial intelligence technology include fraud detection, digital assistants, and market analysis.

artificial intelligence AI neural network
Neural Network as A Branch of Artificial Intelligence (Credit: TseKiChun 2021 .CC BY-SA 4.0.)


4). Natural Language Processing as one of the Branches of Artificial Intelligence

Natural language processing (NLP) is a branch of artificial intelligence that deals with the extraction of information data in either the text or audio formats.

In natural language processing, the computer system is programmed to recognize human interaction patterns, and it uses these patterns to simulate or analyze such human interactions.

 Some applications of natural language processing include chatbots, and personal assistants.


5). Robotics

Robotics is a branch of artificial intelligence which is concerned with the design, development and deployment of robots [10].

The knowledge of various fields, including computer science and engineering, are applicable in robotics [1].

Basically, robots are designed to handle tasks that are commonly handled by humans. These tasks are mostly monotonous and heavy or dangerous.

However, the advancement of artificial intelligence has broadened the scope of utilization of robots, to include more complicated tasks.

The mechanism by which robotics functions, is similar to that of other branches of artificial intelligence. It includes data collection, analysis, and utilization for decision-making.


6). Fuzzy Logic as one of the Branches of Artificial Intelligence

Fuzzy logic is a branch of artificial intelligence that works by assessing the credibility of data, to classify the data as either true or false.

As the name implies, fuzzy logic technology emulates human logical cognitive processes. This branch of AI deals with proving or disproving a hypothesis or uncertain data.

The aim of fuzzy logic is to provide a fair degree of flexibility when tackling uncertainties. It is a supportive technology to other branches of artificial intelligence like machine learning.

In fuzzy logic, data is resolved into true or false results, where true results are represented numerically as 1.0, while false results are represented as 0.0 [4].

Due to the flexibility of fuzzy logic, data can also be classified as partially true and partially false [9]. This makes it possible to analyze a broad range of data categories.



Artificial intelligence is the simulation of human cognitive processes by computers.

The branches of artificial intelligence are;

1). Machine Learning

2). Expert Systems

3). Neural Networks

4). Natural Language Processing

5). Robotics

6). Fuzzy Logic



1). Berenguel, M.; Rodriquez, F.; Moreno, J. C.;  Luis Guzmán, J. (2018). “Tools and methodologies for teaching robotics in computer science & engineering studies.” Computer Applications in Engineering Education 24(2). Available at: https://doi.org/10.1002/cae.21698. (Accessed 4 April 2022).

2). Bhatt, S. (2018). “Reinforcement Learning 101.” Available at: https://towardsdatascience.com/reinforcement-learning-101-e24b50e1d292. (Accessed 4 April 2022).

3). Fumo, D. (2017). “Types of Machine Learning Algorithms You Should Know.” Available at: https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861. (Accessed 4 April 2022).

4). Hellmann, M. (2001). “Fuzzy Logic Introduction.” Available at: https://www.researchgate.net/publication/238684924_Fuzzy_Logic_Introduction. (Accessed 4 April 2022).

5). Johnson, D. (2022). “What is Expert System in AI (Artificial Intelligence)? with Example.” Available at: https://www.guru99.com/expert-systems-with-applications.html. (Accessed 4 April 2022).

6). Kadari, P. (2021). “Introduction to Reinforcement Learning for Beginners.” Available at: https://www.analyticsvidhya.com/blog/2021/02/introduction-to-reinforcement-learning-for-beginners/. (Accessed 4 April 2022).

7). Loukas, S. (2020). “What is Machine Learning: Supervised, Unsupervised, Semi-Supervised and Reinforcement learning methods.” Available at: https://towardsdatascience.com/what-is-machine-learning-a-short-note-on-supervised-unsupervised-semi-supervised-and-aed1573ae9bb. (Accessed 4 April 2022).

8). Selig, J. (2022). “What Is Machine Learning? A Definition.” Available at: https://www.expert.ai/blog/machine-learning-definition/. (Accessed 4 April 2022).

9). Singh, H.; Gupta, M. M.; Meitzler, T.; Hou, Z.; Garg, K. K.; Solo, A. M. G.; Zadeh, L. A. (2013). “Real-Life Applications of Fuzzy Logic”, Advances in Fuzzy Systems, vol. 2013. Available at: https://doi.org/10.1155/2013/581879. (Accessed 4 April 2022).

10). Tyagi, N. (2020). “6 Major Branches of Artificial Intelligence (AI).” Available at: https://www.analyticssteps.com/blogs/6-major-branches-artificial-intelligence-ai. (Accessed 4 April 2022).

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