Guzdial said the project was inspired by online “let's play” videos, in which gamers record or live-stream a gaming session and narrate their progress as they go. Because of his background in game design, Guzdial is often able to recognize the specific coding that's powering the game, just by watching the video
“I noted that even in videos where I had never played the game myself, I was still learning a lot about the game's design,” Guzdial said. “It struck me you might be able to automate this process.”
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The researchers initially trained the AI system on basic “speedrunner” videos where the player's avatar — Mario, say — runs directly toward the goal. This gave the agent an accurate general model of a game using only the video footage. The AI was then able to work backward from the gameplay video to create unique game level designs.
The AI system currently works with Super Mario Brothers, but the researchers have also started replicating the experiments with other popular “platformer” games like Mega Man and Sonic the Hedgehog.
Guzdial and his fellow researchers hope that the new approach will enable human game designers to work with AI agents to create entirely new games. The technique could also have education value for game design students.
“One obvious [educational application] is in teaching people to make games,” he said. “You'd need to have a system that had a lot of knowledge to do something like that, and this is a way to get that knowledge.
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Guzdial said it's important to note that the research, presented last month in Melbourne, Australia at the International Joint Conference on Artificial Intelligence, is only a first step.
“The really cool stuff comes when we have multiple game engines learned in this same representation,” he said. “At that point you can start imagining what combining elements of different game engines can get you. In the future you could imagine the average reader able to generate whole games just by showing our system a few videos.”
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