7 Most Popular Applications of Machine Learning in Game Development

Machine learning is applied in game development in various diverse ways, but here are listed the 7 most popular applications of machine learning in game development.7 Most Popular Applications of Machine Learning in Game Development

Before we discuss the various applications there is a need to look at what machine learning is and how it applies to the development of game software. A good example of the usage of machine learning is in medical diagnosis.

7 Most Popular Applications of Machine Learning in Game Development

Machine learning is a subdivision of artificial intelligence (AI) that enables a system to learn, adapt, automize and improve over time without much programming or coding. To do so and also allow predictable outcomes in software with precise accuracy this machine learning is arranged in algorithms.

What is The Machine Learning Model

These are certain mathematical sequences or data trained into files or software to give certain results. These sequences also recognize similar sequences when posed with them repeatedly and give the same results again. These mathematical sequences are called algorithms. Machine learning uses algorithms to carry out various tasks.

Examples of machine learning in everyday life can be seen in devices like calculators. They can give accurate answers to questions difficult questions that would take the human mind a long time to calculate.

Machine learning is the most important aspect of game development. Here we are discussing the major applications of the innovations of machine learning in application development.;


When playing video games, you find out that there are characters that cannot be controlled by the player. These characters are called NPCs (Non-Playable-Characters) and they respond to certain codes encoded in the game or trained into the models of the game.

They respond in different ways in response to the players’ movement and they become less predictable as the player advances to different or higher levels of the game.

These codes are called algorithms and are arranged in mathematical sequences that are gotten from human players. Therefore, no matter how difficult a game poses at first it is created for the human mind to defeat.

System Modeling

This is where the capacity of the machine learning algorithm comes into play, the sole purpose of game developers is to make games as realistic as possible and also capture the mind of its player.

Therefore, games are now been designed to a certain standard that makes them as real as possible. A good example of this advancement is in FIFA 2022. This game is now developed to make a five-star team having had two players sent off to defeat a team with lower stars altogether. It also allows the cheering up by fans of players to have effects on the vigor and overall morale of the team supported.

More mistakes are common when your teams’ morale is low and outstanding goals are scored from difficult positions by teams playing very well.

Reality Encoding

Capturing the mind of players is an essential part of the development of a game. It increases the appetite of players and enables easy promotion of a game because humans promote what they see as the best quality of its kind.

While playing a game, you see in certain situations that from afar some objects’ image seems very clear, but on getting closer it becomes blurry.

Game developers use machine learning in making reality as real as it can get into games. They try to improve image quality, by improving its dimension and the dynamic nature of images even giving better resolutions. This is achieved by reality coding in machine learning.

Computer Vision Techniques

The game world has posed a sense of reality in video games. When playing a game, you see interactions among non-Playable-characters in scenes that cannot be played either between a player and an NPC character or among the NPCs themselves.

In addition, artificial intelligence (AI) has made it possible for characters to interact visually with another player through a series of coding algorithms. It does this by giving the player a distortion from normal reality into the reality of the game, and these scenes must take place before the continuation of a game by its player.

Some conversations can be short while others are long and could be skipped but others are compulsory because they serve as directions, guides, or lead to the next phase.

Imaging a human player through a virtual reality headset has made it possible for NPCs to interact with the human player and they respond to actions carried out by the player. This is made possible by computer vision techniques through a sequence of algorithms by machine learning.

Natural Language Processing

This is one of the most important parts of machine learning. It allows a system to understand voice commands and given information either in the form of vocals or text as humans do, and give responses to the given information in its way.

NLP in android and ios devices is what gives them the ability to translate one language to another. It also allows Siri for iPhone users to respond to voice commands. These unlimited possibilities are brought through machine learning of natural language processing.

Universe Creation

You must be wondering how a game can be so real in a real sense. Game developers make use of artists in graphic design. Making a game have the dimension of fictional incredibility, games are designed to take their player into a world of possibilities and reality.

Games like grand theft for play stations and call of duty android devices have vast mappings that make the gaming experience easy and interesting.

Convolutional Neural Network (CNN)

This helps you understand incoming data and give out valid information to a recurrent neural network which is responsible for the moves made by the characters in a game.

The convolutional neural network is responsible for the movements of both playable characters and NPCs and this is achieved through building blocks of data arranged into connected layers this is where most of the bulk of the algorithm takes place.

There are many more applications of machine learning, from as small as a mobile phone to as large as tractors used in agricultural farming and other machine learning field.

Types of Machine Learning?

Supervised Learning

This is the most important aspect of the three types of machine learning. It makes use of labeled data as a template or sample data to form a set of data of its own.

One other dynamic nature of supervised learning is its ability to create a vast level of solution after being given a small data template to work with. For instance, if the picture of a dog is taken as labeled data and is used as a template, the color, height, and size of the dog are analyzed and saved as historical data through machine learning by training.

When the sequence of algorithms is built to identify the pictures of many animals including the one of a dog, this algorithm can categorically identify the pictures that contain a dog.


Supervised learning is classified into two categories;

  • Classification deals with the aspect of supervised learning that deals with the classification of items and gives the output categorically, for example, Young or Old, Yes or No, and others. Why;
  • Regression deals with mostly the prediction of items used to give solutions to regression problems like weather forecasts or prediction and others.

Unsupervised Learning

This is the part of machine learning that doesn’t need supervision as it makes use of unlabeled data and gives out its result without supervision. They mainly act as sorting out differences, similarities, and others as output variables. With this machine learning, much labor is not required, unlike unsupervised learning which requires supervision.

Reinforcement Learning

This is the last type of machine learning. It makes use of the trial-and-error method to give out solutions, this way it deletes(discourage) unfavorable data and keeps the required ones by some set of algorithms it gives out the required output, and it is widely used in maps to find shorter cuts to destinations.

What is Machine Learning Used for?

machine learning usage is common among most devices if not all devices that we use that are easier for us to work, they are as follows;

Image Detection

This is one of the most widely used machine learning applications among others. It helps the system recognize the dimension of every image in coding and great details.

In addition, this is what most developed countries use in facial recognition and detection for each citizen in their homeland countries and also android devices use for access to itself.

Voice Detection

You might be wondering how devices get the ability to recognize vocal commands and then respond to them with utmost accuracy.

Voice recognition is widely used in various technologies for example, shazam uses sound identification for finding names or artists for a Particular song just by listening to it and Siri responds to vocal instructions given by its user.


This aspect of machine learning deals with the estimation of various things with precise accuracy on the prediction. It is an operating system used by cabs and uber in the estimation of the cost of a particular journey.

so even if the journey is not completed by the individual, because of a matter of urgency like traffic. It calculates the cost of the journey made so far by the individual and not the cost of the completed journey.

Video Surveillance

Machine learning is also used in recording events after their occurrence. It can be used to solve crime incidence and also detect awkward movements of individuals in a particular area or place. It allows the observation of cars in traffic, in cases of breaking the law.

Social Media Platform

You will discover that when you visit a site often, maybe a Youtube video that talks about a topic or Facebook, related searches to that site will pop up on your device either as a notification or page.

Facebook usually suggests friends common to your friends on Facebook as notifications most often than not. This is only possible by machine learning; it also brings to your attention videos or pictures related to a particular subject you once visited.

Spam and Malware

Our different systems are exposed to Malware and they are detected by machine learning. We are exposed to quite a several unwanted junk files on our devices, and one of the different types of machine learning helps to solve this problem.

For instance, when running a mail account, you can be exposed to fraudsters. The machine learning tools help to solve this problem by placing unwanted documents sent by the fraudster into spam files. It will notify you that the file is from an unreliable source or a bulk file sent to you and many other persons.

Customer Support

This is a vital part of tech companies or any establishment that has the desire to reach its peak. It is so because customers are posed with difficulties before usage or after the usage of a particular product.

Customer support is established to help customers with better and improved services and also help to drop their complaints on their pages, apps, or websites, and immediate responses can be given to the individual to address their complaint.

The response can either be by a person or by chatbots. The answers not programmed for chatbots to answer are responded to by the organization, website, or app immediately or later on, these chatbots are programmed as a result of machine learning.  

Search Engines

Many websites provide solutions to human problems. They help with the best answers to questions of all the major sectors of life and they remove the irrelevant ones, with the help of machine learning algorithms. One of the most visited search engines is Google.

Application and Companies

Machine learning has made the world a better place in its way. Quality service can now be rendered without human assistance, and you can be part of the discussion or seminal without actually being present.

You can purchase things online and have them delivered to your doorstep, this is all possible because of machine learning. Models are designed into layers to store information, process them and give out quality output.     

Frequently Asked Questions

What is Machine Learning for Example?

Machine learning is the act of telling a program or software what to do even in your absence. it is possible by a series of arranged mathematical sequences into an algorithm to produce certain results.

For example, the vulnerabilities of passwords to private accounts are brought into the notification of the individual by machine learning, Google passwords are good example of this.

When a weak password is used for the protection of a particular data, google gives suggestions of different types of stronger passwords that can be used for efficient protection and can also be used to notice cyberattacks and data breaches.

Vital applications of machine learning?

There are vast applications of machine learning, these are some of the most important ones that affect our daily life;

  • Virtual Personal Assistant
  • Cyberattacks Detection
  • Social Media Platforms
  • Language Translation
  • Risk Evaluation
  • Transportation and Dynamic Pricing
  • Commuting

Machine learning applications in the industry?

Programmed Chatbots are one of the most valuable uses of machine learning in the industry. Chatbots are created to make the best service available to customers. Most questions asked by customers are the same, so chatbots reply to a particular question asked by a great number of people instantly without human intervention.

Machine learning is also used in Price estimation and is widely used by transport companies like Uber and Cabs, it allows a dynamic way of prices.

For instance, when you book a cab for a certain journey, there is a normal price attached to that journey but if its customer decides to stop halfway through the journey its dynamic nature allows the customer entitled to half the price of the journey, and this is made possible only by machine learning.

What are the applications of unsupervised learning?

Areas, where unsupervised learning is used, are;

  • Recommendation
  • Differences and similarities detection
  • Products Arrangements
  • Labeling of Unlabeled data

Machine learning applications in healthcare?

Machine learning is used in the medical sector in identifying diseases for diagnosis. Sample data is used as a template for the behavior of a microscopic pathological organism and is, therefore, able to distinguish between healthy microscopic organism data from a diseased one.



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