Currently, opponents in a video game are pre-scripted NPCs (non-playable characters), whereas a machine learning NPC could allow users to play against less predictable foes, making the game much more interesting.

Companies are already working on early applications of machine learning in NPCs.

More engaging mobile games

Mobile games have contributed to 50% of the revenue generated by video games. The scope of these games is limited because of the hardware of smartphones. But after the implementation of AI and ML integrated chips into smartphones, this situation has started to change.

Realistic interactions

One of the major challenges in game development is building a realistic virtual world to help players interact with NPCs. Implementing NLP could allow users to talk out loud to in-game characters and get real responses. It will be like talking to Siri, Alexa, or Google Assistant.

Modeling complex systems

A machine learning algorithm's strength is its ability to model complex systems. Video game developers are relentlessly trying to make gaming more immersive and realistic. Indeed, modelling the real world is extremely difficult, but ML algorithms can help create these complex models that players cannot control.

Assisted artwork generation

Games generally consist of several assets that are all produced similarly. ML techniques can help optimise workflows so that the artists can spend more time on the creative parts of their work and manage less time on the mechanical parts.

Enhancing developer skills

The traditional video game developers can skill up their ML techniques with the growing demand in the industry. The technologies and innovation taking up the game development industry will include machine learning. Therefore, game designers can practice both to become more efficient.