![]() In 500 games against other available Go programs, including Crazy Stone and Zen, AlphaGo running on a single computer won all but one. ![]() AlphaGo represents a significant improvement over previous Go programs. Īccording to DeepMind's David Silver, the AlphaGo research project was formed around 2014 to test how well a neural network using deep learning can compete at Go. In 2013, Crazy Stone beat Yoshio Ishida (9p) at a four-stone handicap. In 2012, the software program Zen, running on a four PC cluster, beat Masaki Takemiya ( 9p) twice at five- and four-stone handicaps. Īlmost two decades after IBM's computer Deep Blue beat world chess champion Garry Kasparov in the 1997 match, the strongest Go programs using artificial intelligence techniques only reached about amateur 5-dan level, and still could not beat a professional Go player without a handicap. Go is considered much more difficult for computers to win than other games such as chess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alpha–beta pruning, tree traversal and heuristic search. The self-taught AlphaGo Zero achieved a 100–0 victory against the early competitive version of AlphaGo, and its successor AlphaZero is currently perceived as the world's top player in Go. Īfter the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas. Īt the 2017 Future of Go Summit, the Master version of AlphaGo beat Ke Jie, the number one ranked player in the world at the time, in a three-game match, after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association. The win by AlphaGo was chosen by Science as one of the Breakthrough of the Year runners-up on 22 December 2016. The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled AlphaGo, directed by Greg Kohs. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association. Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicap. In October 2015, in a match against Fan Hui, the original AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 19×19 board. This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration. A neural network is trained to identify the best moves and the winning percentages of these moves. AlphaZero has in turn been succeeded by a program known as MuZero which learns without being taught the rules.ĪlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play. AlphaGo Zero was then generalized into a program known as AlphaZero, which played additional games, including chess and shogi. After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as AlphaGo Zero, which was completely self-taught without learning from human games. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. It was developed by the London-based DeepMind Technologies, an acquired subsidiary of Google (now Alphabet Inc.). ![]() AlphaGo is a computer program that plays the board game Go.
0 Comments
Leave a Reply. |