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Good at StarCraft? DARPA Wants to Train Military Robots with Your Brain Waves
Researchers at the University at Buffalo have developed a strategy game whose results can support the development of new algorithms that can help train large numbers of future robots.
Credit: Douglas Levere/University at Buffalo
The 1984 movie The Last Starfighter tells the story of a teenager whose calling in life seems to be nothing more than to play arcade games. Fortunately, he's spectacularly good at it. The game he's best at is a video game called, as the movie's title would have it, Starfighter. In it, the player must defend their homestead, The Frontier, from the perils of Xur and the Ko-Dan Armada by way of a series of wireframe laser battles.
But there's a twist. It turns out that Starfighter isn't simply a game; it's actually a kind of test. The war with Xur and the Ko-Dan Armada is real, and the arcade game — with its demands on rapid-fire reaction times on the part of players — is a stealth recruiting tool, intended to seek out the best of the best to become genuine starfighters.
More than 35 years after The Last Starfighter hit theaters, engineers from the University at Buffalo, New York, Artificial Intelligence Institute have received funding from DARPA, the U.S. Defense Advanced Research Projects Agency, to carry out research that's… well, let's just say that it's extremely similar. They have built a real-time strategy game, currently unnamed, that's reminiscent of existing games like StarCraft or Stellaris in style. In this game, players must use resources to build units and defeat enemies; manipulating large numbers of agents on-screen to complete their mission objectives.
But this isn't any ordinary gaming experience. When people play the University at Buffalo's new strategy game, they first have to agree to be hooked up to electroencephalogram (EEG) technology so that the game's designers can record their brain activity. As they play, their eye movements are also tracked by way of special ultra high-speed cameras to see exactly how they respond to what they're doing. This information, which can be teased out using machine learning algorithms, will then be used to develop new algorithms that can help train large numbers of future robots. In particular, the hope is that these insights into complex decision-making can improve coordination between large teams of autonomous air and ground robots. You know, should the game be brought to life.
From Digital Trends
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