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6 Ways Machine Learning has revolutionized Video Game Industry

  • Last Updated : 07 Jun, 2019
Geek Week

The global video-game industry has been on the rise ever since the introduction of smartphones and video game consoles like the Xbox and the Playstaion. In 2018, the industry was valued at $139 billion. This is already bigger than the Film, Television and Music industries combined. Combined across all platforms, the Gaming industry now boasts of 2.3 billion gamers worldwide. Video games are also one of the most profitable forms of entertainment. This can be easily visualized from the fact that Rockstar Games’ Grand Theft Auto V raked in $6 billion in total revenues. The reasons for the popularity of such products are that they create an Immersive World, Capture the player’s imagination and Provide with unique playable content.

Behind the success of these video-games, there is a lot of labor-intensive work which is done by the developers of the studios. Every little nuance of each character and object in the character’s environment has to be hand coded. This repetitive work takes up a lot significant part of a game’s development time.

With the recent advances in the Graphic Processor Units (GPU) and the large volumes of playing data, a new suggestion has come up – To incorporate Machine Learning and other Artificial Intelligence technologies into the development process. This can enable the game engine to respond to the player’s actions dynamically and to cut out the time taken by the hand-encoding of little things in the environment.
With the incorporation of Machine Learning in the game development process, the following changes can and have been observed in the gaming experience –

Reactive Non-Playable Characters:
Generally, the Non-Playable Characters have to be hard-encoded in the sense that they are scripted characters which respond to fixed situation with a fixed set actions. With the incorporation of Machine Learning, these characters can adapt according to the environment and the player’s game-style. For example, in the game Metal Gear Solid 5: The Phantom Pain produced by Kojima Productions, if a player continuously uses the technique of headshots in the game, the characters adapt to it by starting to “wear” helmets to prevent to getting hit on the head.

Modeling Complex Systems:
To make a more immersive and practical world, developers are using the inherent strength of Machine Learning algorithms’ to make predictive models to predict the downstream of a player’s action. For example, in EA Sports’ FIFA 19, the result and the venue of the game can affect a team’s morale and thus affecting it’s playing abilities.

More Beautiful Games:
The general problem with video-game graphics is that while they look good from a distance, they render poorly when viewed from proximity. Microsoft is working with Nvidia so that images can render dynamically and finer details can be seen when seen from close.

Realistic Interactions:
Typical open-world games require the player to interact with it’s environments and “fellow-men” to complete the objectives. With the rise of Natural Language Processing, the player can interact with other characters in a more realistic manner. For example, in Rockstar Games’ Red Dead Redemption 2, one has to maintain the ‘honour’ level of our character and the other in-game characters interact with the player according to this rating.

Dynamic Universe Creation:
Most of the popular games in the industry are the ‘open-world’ games which allow the player to interact with the environment. But this universe-creation takes a lot of time to be perfect and consists of repetitive and small tasks. With the help of Machine Learning, the time taken by the process has reduced manifold and the developers can utilise the time saved in more creative processes.

Engaging Mobile Games:
Mobile Games contribute about 50% of the revenue generated by video-games. The scope of these games are limited because of the hardware of the smartphones. But this situation has started to change as now AI chips are getting installed into the latest smartphones. The best example is that of OnePlus 7 Pro. It’s hardware specifications are so keeping in mind the mobile gaming industry.

Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.

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