Researchers from Georgia Tech demonstrated utilization of A.I. for creating brand-new video games after being shown hours of classic 8-bit gaming action for inspiration
In recent past, Google DeepMind demonstrated the ability of Artificial Intelligence (AI) to play retro video games better than the majority of human players, without requiring any instruction as to how they should accomplish the feat. “Our system operates in several stages,” said Mark Riedl, associate professor of Interactive Computing at Georgia Tech. “First, we take video of several games being played. In this case, the games are Super Mario Bros., Kirby, and Mega Man. Our system learns models of the level design and game mechanics and rules for each game. The machine learning algorithms we use are probabilistic graphical models for learning level design, and a form of causal inference for learning game mechanics.”
These models were used to generate new games, and the resulting games are just like the ones that inspired them. Instead, their algorithm carries out ‘conceptual expansion,” which infers the existence of models for games that do not exist, but potentially could, based on what is learned from the input game video. Furthermore, A.I. generates games that fall into the overall hypothetical game models — bearing enough resemblance to other titles’ game mechanics to be familiar, but not exact copies.
For instance, in the game Death Bounce, the system has learned that some objects disappear when hit from above, and applies this concept to the ground instead of enemies. In the fame Killer Walls, the A.I. creates an enemy wall based on a combination of its understanding of enemies and wall obstacles. Riedl and Ph.D. student Matthew Guzdial are curious to find out whether machines can play a role in carrying out creative acts, such as designing video games. “Games are really complex and really hard to make, even for experts, so the ability of an algorithm to create interesting, working games is a notable achievement,” Riedl said.