Machine Learning in Video Games

Machine Learning in Video Games

Jayakumar Kaliappan (Vellore Institute of Technology, Vellore, India) and Karpagam Sundararajan (Spectrum Info Tech, India)
DOI: 10.4018/978-1-5225-9643-1.ch020
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Machine learning is a part of artificial intelligence in which the learning was done using the data available in the environment. Machine learning algorithms are mainly used in game development to change from presripted games to adaptive play games. The main theme or plot of the game, game levels, maps in route, and racing games are considered as content. Context refers to the game screenplay, sound effects, and visual effects. In any type of game, maintaining the fun mode of the player is very important. Predictable moves by non-players in the game and same type of visual effects will reduce the player's interest in the game. The machine learning algorithms works in automatic content generation and nonpayer character behaviours in gameplay. In pathfinding games, puzzle games, strategy games adding intelligence to enemy and opponents makes the game more interesting. The enjoyment and fun differs from game to game. For example, in horror games, fun is experienced when safe point is reached.
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Need For Machine Learning In Game Development

Algorithms Playing As Nonplayer Characters

When playing with pre-scripted Non-Playable-Characters (NPC), there won’t be any surprises and it makes the game boring, but a machine learning-based NPC gives you a challenging playground with unpredictable foes. In the machine learning algorithm, Non-Player Characters (NPC) behavior are used to optimize the dynamic difficulty adjustment feature. Avoiding the hard coding of Non-Player characters would reduce the development cycle time from days to hours. NPC’s will become smarter and smarter when more about data the player’s behavior on the particular NPC is gathered.

Modelling Complex Systems

The machine-learning algorithm has a high ability to model complex systems which make games to be more impressive and realistic. To bring in the realistic environment, the players’ emotions and the audience reactions were learned from the real games and it will get applied in our game at required points. Machine learning algorithms are able to predict the downstream effects of player actions. The FIFA game is the most successful game in modeling the complex system. In this, a team’s chemistry is calculated based on the number of players go along with each other.

Making Games More Beautiful

The game scenes have to be designed attractive according to human vision the objects in the distance will not be clearer, but when it is brought into the nearer focus the clarity of the object increases. Computer vision algorithm powered with machine learning is in development so that the real-time, dynamic rendering of images with high clarity is viewed.

More Realistic Interactions

The real environment will be created, when the player is able to communicate with the NPC’s in a natural way. It will improve the fun level and the involvement of the player also. In video games, the techniques used by the player to interact with his speech or the body movements, which mimic the same task used in the real world are called Natural interaction techniques. With the help of natural language processing, voice-based interaction with the NPC’s can be done. In some games, the story is designed to get some secrets from the friendly NPCs after crossing the levels. This interaction will be script formatted, but to make it very real the voice-based communication can be used. Like Alexa and Google Assistant more friendly atmosphere is created. When there is a use of a sword in a game, when swinging of the sword is done in a natural way with hand movements it will be interesting rather than searching for a button click.

Universe Creation on the Fly

The most loved video games, nowadays are the games where the player enjoy exploring the massive landscape. Machine learning algorithms operate in this open-world designs to solve the struggles in finding the path and in improvising world creation mechanisms. Machine learning algorithms could help with pathfinding and world creation. Endless games are created in real-time. The new environment creation is based on player profiles and values. So the contents are created which fits in to the respective user’s profiles. Hence every player will get attracted into the game and also enjoy the game.

More Engaging Mobile Games

As the power of the hardware in mobile phones continues to improve, making mobile gaming more realistic, interactive, and immersive with machine learning will be an easier job. Reducing the game space is very important and this is possible with machine learning. The machine learning algorithms are able to produce novel contents, unlike the pre-scripted games which will depict only the contents that are previously loaded into the game memory.

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