Designers are prototyping the addition of deep learning that will train bot behavior across multiple games.
, has spent nearly five years using AI to test games. Modl.ai uses bots to hunt graphical glitches, find flaws in world geometry, and sniff out situations that make it impossible to win.
Modl.ai’s testing bots use machine learning to adapt to each game tested, though its current implementation limits those adaptations to each specific title. Togelius says the company is prototyping the addition of deep learning that will train bot behavior across multiple games. Once in use, Modl.ai’s bots will learn to emulate the behavior of real players, which should more efficiently uncover issues that players would find.
“Yes, performance is clearly an issue, notably with large ML models that process frames for each tick of the game clock,” Trachel and Peyrot said in an email. “In our case, to avoid performance issues, we used a small neural network that was only inferring at precise moments of the game.” Scaling up to the huge open-world environments that modern players expect is another matter entirely.
Animation is another issue. Most modern game engines expect animations to be strictly defined frame by frame. This works well when animators know with certainty how game characters will behave, but an AI controlled by machine learning might behave in ways the animators didn’t expect. Designers can work around this, but this places even more performance strain on a game console or computer’s hardware and comes with its own development challenges.
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