Top 10 Uses of ML to Make Your Video Games More Engaging



by Sayantani Sanyal

20 October 2021

Machine learning is revolutionizing almost everything from agriculture to cancer diagnostics in healthcare. It transformed the way traditional business organizations operated and spurred their growth in no time. Recently, the gaming industry has also adopted machine learning algorithms to make video games more engaging. ML is used for high speed game development. It’s a powerful tool for game developers to create more realistic worlds, fascinating challenges, and unique content. Unfortunately, the use of ML in game development is still in its infancy and hasn’t made the headlines in the same way. In this article, we discuss the various ways in which machine learning has revolutionized video game development.

• Modeling of complex systems: The strength of a machine learning algorithm is its ability to model complex systems. Video game developers are relentlessly trying to make games more immersive and realistic. Indeed, modeling the real world is extremely difficult, but ML algorithms can help create those complex models that gamers cannot control.

• Realistic interactions: One of the main challenges in game development is creating a realistic virtual world to help players interact with NPCs. The implementation of NLP could allow users to speak aloud to the characters in the game and get real answers. It will be like talking to Siri, Alexa, or Google Assistant.

• Dynamic audio edits: Some parts of the game’s development scheme can be incredibly long and difficult to change after being produced. In addition, speech generation with machine learning can help correct audio edited to allow script changes or insert the player’s name into a pre-recorded dialogue. In the long run, AI voice actors could even replace real-life actors, especially for supporting characters.

• Custom User Content: Machine learning technologies offer fascinating opportunities to create systems that can be used directly by users to generate content that matches the style of the game itself. They offer gamers the option to take their picture and add them to games based on their likeness.

• ML algorithms playing as an NPC: Currently, a video game’s opponents are pre-scripted NPCs (non-playable characters), whereas a machine-learning NPC could allow users to play against less predictable enemies, making the game much more interesting. Companies are already working on the first applications of machine learning in NPCs. Algorithms can train NPC players four times faster than reinforcement training alone.

• Creation of dynamic universe: Most of the popular video games in the industry are open-world games that allow players to interact with the environment. But creating this interface takes a long time to be perfect and consists of repetitive, non-essential tasks. This time-consuming process became effective after ML implementation because it reduced the variety and developers can use the time for more creative pursuits.

• More engaging mobile games: Mobile games have contributed 50% of the revenue generated by video games. The scope of these games is limited due to the hardware of the smartphones. But after the implementation of integrated AI and ML chips in smartphones, this situation started to change.

• Can adjust difficulty levels according to player preferences: Another virtue of ML-designed video games is gamer experience modeling, which means providing tailored experiences to gamers based on their level of expertise. So, if the player is a beginner, the ML algorithm will adjust the difficulty level in easy mode so that the player does not get frustrated.

• Assisted illustration generation: Games usually consist of multiple assets which are all produced in the same way. ML techniques can help optimize workflows so artists can spend more time on the creative parts of their work and manage less time on the mechanical parts.

• Improve developer skills: Traditional video game developers can hone their ML techniques with the growing demand from the industry. Technologies and innovation in the game development industry will include machine learning. Therefore, game designers can practice both to become more efficient.

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