Algorithm For Chess Program Reviews
20 years after DeepBlue defeated Garry Kasparov in a match, chess players have awoken to a new revolution. The AlphaZero algorithm developedby Google and DeepMind took just four hours of playing against itself tosynthesise the chess knowledge of one and a half millennium and reach a levelwhere it not only surpassed humans but crushed the reigning World Computer ChampionStockfish 28 wins to 0 in a 100-game match.
All the brilliant stratagems andrefinements that human programmers used to build chess engines have beenoutdone, and like Go players we can only marvel at a wholly new approach to thegame. After DeepMind's AlphaZero the chess engine world, and the chess world, will never be quite the same againOnly five days ago, in a more innocent world, IanNepomniachtchi could say after Round 1 of the London Chess Classic at GoogleHeadquarters:I hope there will be some big history of cooperation betweenGoogle and chess. It’s not about creating an AlphaGo, an AlphaChess, which willkill chess, but maybe in some friendly mode.There were worrying signs, though, as AlphaGo, the programthat defeated the human World Champion, had just been surpassed by AlphaGoZero,which learned the game merely by playing itself.
The contents are stunning. The DeepMind team had managed toprove that a generic version of their algorithm, with no specific knowledgeother than the rules of the game, could train itself for four hours at chess,two hours in shogi (Japanese chess) or eight hours in Go and then beat thereigning computer champions – i.e. The strongest known players of those games.In chess it wasn’t just a beating, but sheer demolition.Stockfish is the reigning TCEC computer chess champion, andwhile it failed to make the final this year it went unbeaten in 51 games. In amatch with the chess-trained AlphaZero, though, it lost 28 games and won none,with the remaining 72 drawn. With White AlphaZero scored a phenomenal 25 winsand 25 draws, while with Black it “merely” scored 3 wins and 47 draws. It turns out the starting move is really important after all!In the paper DeepMind share 10 of the wins againstStockfish, which we’ve added below so you can replay them with some slightlylower level computer analysis (simply click on a result). Instead the algorithm lived up to its name by starting from zero apart from the rules of the game.
Then it began to play games using aMonte-Carlo algorithm, where initially random moves would be tried out until aneural network began to learn which options were likely to be more promising.It was only a couple of months ago that the former Top 10 player:Until 2015 that was the only intellectual game in whichprofessionals were stronger than machines, and only in the last year or yearand a half have the first harbingers appeared saying that yes, the end of Gohas come. For now it’s not quite formalised, but gradually, I think, they’llfollow the same path that we followed in chess. Machines, of course, will takeup an absolutely dominant position, despite the fact that of course thecalculating algorithms, the evaluation algorithms are quite different. As faras I understand it the algorithm used by AlphaGo, the most successful program,is a Monte Carlo algorithm.
That was also one of the main computationalapproaches in chess, but it didn’t become common. Machines reached a maximum of2400 with that.
After all, our game is about more direct selection, while thereit was possible even to use that algorithm, which is quite interesting.It turns out the approach may have been right after all,though the game-changer is perhaps phenomenal hardware. They used to say you needed 10,000 hours of deliberate practice to master something.During training AlphaGohad access to, “5,000 first generation TPUs to generate self-play games and 642nd-generation TPUs to train the neural networks”. TPUs, or, aren’t even publicly available, since they were developed by Googlespecifically to handle the kind of calculations demanded by machine learning. Thetrained algorithm, meanwhile, was run on a single machine with four TPUs, andDeepMind stress the efficiency of their approach, with AlphaZero searching just80,000 positions per second compared to 70 million for Stockfish. How does it achieve thatefficiency?AlphaZero compensates for the lower number of evaluations byusing its deep neural network to focus much more selectively on the mostpromising variations – arguably a more “human-like” approach to search, asoriginally proposed by Shannon. Figure 2 shows the scalability of each playerwith respect to thinking time, measured on an Elo scale, relative to Stockfishor Elmo with 40ms thinking time. AlphaZero’s MCTS scaled more effectively withthinking time than either Stockfish or Elmo, calling into question the widelyheld belief that alpha-beta search is inherently superior in these domains.
The stats under the chessboards refer to another 1200 'opening themed' games vs. Stockfish - the total score was 290 wins, 886 draws & 24 losses for AlphaGo, or 733:467The graphs are fascinating to study, since you cansee how certain openings became popular in the algorithm’s training games –such as the French Defence and the Caro-Kann – before dropping off inpopularity as its strength increased. Note it looks as though there’s a reasonfor the popularity of the Queen’s Gambit at the very highest level, and that of another somewhat notorious opening.
Where do we go from here?What happens next will depend largely on how keen DeepMindare on keeping their chess-trained algorithm active. Will it be “dismantled”like DeepBlue, or will it instead become available, freely or at a price, forchess players. You can imagine that the elite grandmasters, desperate for anyedge they can find, would dearly love to get their hands on it. Will it soon bepossible to use the “engine” alongside existing software to give evaluationsand potential moves in games?And where do traditional chess programmers go from here?Will they have to give up the refinements of human-tuned evaluation functionsand all the existing techniques, or will the neural networks still requireprocessing power and equipment not easily available? Will they be able tofollow in DeepMind’s footsteps, or are there proprietary techniques involved that can’teasily be mastered?There’s a lot to ponder, but for now the chess world hasbeen shaken!
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Algorithm For Chess Program Reviews 2018
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Best Chess Software 2018
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Stockfish
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