먹튀검증 커뮤니티 have long served as excellent platforms for testing AI algorithms. A game’s structure, repetition and reinforcement provide an ideal environment for training AI. And a game’s simulated physical space can be more precise and scalable than the real world, allowing researchers to more quickly test their algorithms.
However, the most promising use cases for AI in games aren’t just about speeding up tasks and enhancing gameplay. They can also make the gaming experience more engaging by bringing the characters and environment to life.
Behind the Scenes: Training AI for Competitive Play
For example, some recent games use adaptive AI to improve the overall experience by adapting in-game settings and challenges based on player performance. For example, if an AI notices that it has a low health level, it might run away or hide until it can reload. This makes the AI more human and gives players a feeling of personal responsibility for their actions.
Other games are using generative AI to create more realistic NPCs. NPCs typically tend to be quite simplistic, repetitive and expressionless. Generative AI could create NPCs that behave more naturally and have a wider range of expressions, leading to greater immersion and making the games feel more alive.
If you’re interested in trying some of these AI games for yourself, check out Ludo’s free online collection of fun, experimental AI-generated video games. Or try Google’s Arts and Culture Lab’s generative AI, which can generate computer code for a video game. (Remember that it takes a lot of data to train an AI to play a video game well, though. John Hester, a retired software developer from Southern California, needed 500,000 hours of gameplay to get his AI-powered Pac-Man to compete with his friend’s.)