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Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty.

“Its a kind of creation,” he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion.

Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote. “For quite a number of games on the highest level, half of the game—sometimes a full game—is played out of memory,” Kramnik says. “You dont even play your own preparation; you play your computers preparation.”

Wednesday, Kramnik presented some ideas for how to restore some of the human art to chess, with help from a counterintuitive source—the worlds most powerful chess computer. He teamed up with Alphabet artificial intelligence lab DeepMind, whose researchers challenged their superhuman game-playing software AlphaZero to learn nine variants of chess chosen to jolt players into creative new patterns.

In 2017, AlphaZero showed it could teach itself to roundly beat the best computer players at either chess, Go, or the Japanese game shogi. Kramnik says its latest results reveal beguiling new vistas of chess to be explored, if people are willing to adopt some small changes to the established rules.

The project also showcased a more collaborative mode for the relationship between chess players and machines. “Chess engines were initially built to play against humans with the goal of defeating them,” says Nenad Tomašev, a DeepMind researcher who worked on the project. “Now we see a system like AlphaZero used for creative exploration in tandem with humans rather than opposed to them.”

People have played chess for around 1,500 years, and tweaks to the rules arent new. Neither are grumbles that computers have made the game boring.

New rules

Chess spread rapidly around 500 years ago after European players promoted a slow-moving piece into the powerful modern-day queen, giving the game more zip. In 1996, one year before IBMs Deep Blue defeated Kasparov, chess wunderkind-turned-fugitive Bobby Fischer called a press conference in Buenos Aires and complained that chess needed a redesign to demote computer-enhanced memorization and encourage creativity. He unveiled Fischer Random Chess, which preserves the usual rules of play but randomizes the starting positions of the powerful pieces on the back rank of the board each game. Fischer Random, also known as Chess960, slowly earned a niche in the chess world and now has its own tournaments.

DeepMind and Kramnik tapped AlphaZeros ability to learn a game from scratch to explore new variants more quickly than the decades or centuries of human play that would reveal their beauty and flaws. “You don't want to invest many months or years of your life trying to play something, only to realize that, Oh, this just isn't a beautiful game,” says Tomašev.

AlphaZero is a more flexible and powerful successor to AlphaGo, which laid down a marker in AI history when it defeated a champion at Go in 2016. It starts learning a game equipped with only the rules, a way to keep score, and a preprogrammed urge to experiment and win. “When it starts playing its so bad I want to hide under my table,” says Ulrich Paquet, another DeepMind researcher on the project. “But seeing it evolve from a void of nothingness is exciting and almost pure.”

In chess, AlphaZero initially doesnt know it can take an opponents pieces. Over hours of high-speed play against successively more powerful incarnations of itself, it becomes more skilled and, to some eyes more natural than prior chess engines. In the process, it rediscovers ideas seen in centuries of human chess and adds flair of its own. English grandmaster Matthew Sadler described poring over AlphaZeros games as like “discovering the secret notebooks of some great player from the past.”

Former chess world champion Vladimir Kramnik, left, worked with Alphabet's DeepMind, founded by Demis Hassabis, right, to explore new forms of chess using artificial intelligence.
Enlarge / Former chess world champion Vladimir Kramnik, left, worked with Alphabet's DeepMind, founded by Demis Hassabis, right, to explore new forms of chess using artificial intelligence.Deepmind

The nine alternative visions of chess that AlphaZero tested included no-castling chess, which Kramnik and others had already been thinking about and which had its first dedicated tournament in January. It eliminates a move called castling that allows a player to tuck their king behind a protective screen of other pieces—powerful fortification that can also be stifling. Five of the variants altered the movement of pawns, including torpedo chess, in which pawns can move up to two squares at a time throughout the game, instead of only on their first move.

One way of reading AlphaZeros results is in cold numbers. Draws were less common under no-castling chess than under conventional rules. And learning different rules shifted the value AlphaZero placed on different pieces: under conventional rules, it valued a queen at 9.5 pawns; under torpedo rules, the queen was only worth 7.1 pawns.

But is it fun?

DeepMinds researchers were ultimately more interested in the analysis of the other great chess brain on the project, Kramnik. “This is not about numbers, but whether it is qualitatively, aesthetically pleasing for humans to sit down and play,” says Tomašev. A technical paper released Wednesday includes more than 70 pages of commentary by Kramnik on AlpRead More – Source

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