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By BeauHD from Slashdot's holy-grail department:
December 06 '18 at 06:51 PM
DeepMind has created a system that can quickly master any game in the class that includes chess, Go, and Shogi, and do so without human guidance. "The system, called AlphaZero, began its life last year by beating a DeepMind system that had been specialized just for Go," reports IEEE Spectrum. "That earlier system had itself made history by beating one of the world's best Go players, but it needed human help to get through a months-long course of improvement. AlphaZero trained itself -- in just 3 days." From the report: The research, published today in the journal Science, was performed by a team led by DeepMind's David Silver. The paper was accompanied by a commentary by Murray Campbell, an AI researcher at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y. AlphaZero can crack any game that provides all the information that's relevant to decision-making; the new generation of games to which Campbell alludes do not. Poker furnishes a good example of such games of "imperfect" information: Players can hold their cards close to their chests. Other examples include many multiplayer games, such as StarCraft II, Dota, and Minecraft. But they may not pose a worthy challenge for long.
DeepMind developed the self-training method, called deep reinforcement learning, specifically to attack Go. Today's announcement that they've generalized it to other games means they were able to find tricks to preserve its playing strength after giving up certain advantages peculiar to playing Go. The biggest such advantage was the symmetry of the Go board, which allowed the specialized machine to calculate more possibilities by treating many of them as mirror images. The researchers have so far unleashed their creation only on Go, chess and Shogi, a Japanese form of chess. Go and Shogi are astronomically complex, and that's why both games long resisted the "brute-force" algorithms that the IBM team used against Kasparov two decades ago.