Difference between revisions of "Go"

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==2020 ...==
==2020 ...==
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588 <ref>[https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?fbclid=IwAR3mSwrn1YXDKr9uuGm2GlFKh76wBilex7f8QvBiQecwiVmAvD6Bkyjx-rE MuZero: Mastering Go, chess, shogi and Atari without rules]</ref>
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588 <ref>[https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?fbclid=IwAR3mSwrn1YXDKr9uuGm2GlFKh76wBilex7f8QvBiQecwiVmAvD6Bkyjx-rE MuZero: Mastering Go, chess, shogi and Atari without rules]</ref>
* [[Tristan Cazenave]], [[Julien Sentuc]], [[Mathurin Videau]] ('''2021'''). ''Cosine Annealing, Mixnet and Swish Activation for Computer Go''. [[Advances in Computer Games 17]]
=Forum Posts=
=Forum Posts=

Revision as of 21:32, 30 November 2021

Home * Games * Go

19*19 Go board [1]

The game of Go has attracted game researchers and programmers as an ambitious AI-challenge. Albert Zobrist was a pioneer, who wrote the first Go program in 1968 as part of his Ph.D. Thesis on pattern recognition [2]. Chess programmers, beside others, Rémi Coulom and Gian-Carlo Pascutto became successful Go programmers with their programs CrazyStone and Leela respectively. Competitive computer Go, as organized by the ICGA [3], is played on boards with 9x9 as well with default 19x19 grids. Since Go lacks a simple evaluation function mainly based on counting material, attempts to apply similar techniques and algorithms as in chess were less successful. The breakthrough in computer Go was accomplished by Monte-Carlo tree search and deep learning.


Monte-Carlo Go

After early trials to apply Monte Carlo methods to a Go playing program by Bernd Brügmann in 1993 [4], recent developments since the mid 2000s by Bruno Bouzy [5], and by Rémi Coulom, who coined the term Monte-Carlo Tree Search [6], in conjunction with UCT (Upper Confidence bounds applied to Trees) introduced by Levente Kocsis and Csaba Szepesvári [7], led to a breakthrough in computer Go [8].


As mentioned by Ilya Sutskever and Vinod Nair in 2008 [9], convolutional neural networks are well suited for problems with a natural translation invariance, such as object recognition. Go has some translation invariance, because if all the pieces on a hypothetical Go board are shifted to the left, then the best move will also shift (with the exception of pieces that are on the boundary of the board). Many applications of neural networks to Go have already used convolutional neural networks, such as Nicol N. Schraudolph et al. [10], Erik van der Werf et al. [11], and Markus Enzenberger [12], among others.

In 2014, two teams independently investigated whether deep convolutional neural networks [13] could be used to directly represent and learn a move evaluation function for the game of Go. Christopher Clark and Amos Storkey trained an 8-layer convolutional neural network by supervised learning from a database of human professional games, which without any search, defeated the traditional search program Gnu Go in 86% of the games [14] [15] [16] [17] [18]. In their paper Move Evaluation in Go Using Deep Convolutional Neural Networks [19], Chris J. Maddison, Aja Huang, Ilya Sutskever, and David Silver report they trained a large 12-layer convolutional neural network in a similar way, to beat Gnu Go in 97% of the games, and matched the performance of a state-of-the-art Monte-Carlo Tree Search that simulates a million positions per move [20].


In 2015, a team affiliated with Google DeepMind around David Silver, Aja Huang, Chris J. Maddison, and Demis Hassabis, supported by Google researchers John Nham and Ilya Sutskever, build a Go playing program dubbed AlphaGo, combining Monte-Carlo tree search with their 12-layer networks [21], the “policy network,” to select the next move, the “value network,” to predict the winner of the game. The neural networks were trained on 30 million moves from games played by human experts, until it could predict the human move 57 percent of the time. AlphaGo achieved a huge winning rate against other Go programs, and defeated European Go champion Fan Hui [22] in October 2015 with a 5 - 0 score [23] On March 9 to 15, 2016, AlphaGo won a $1M 5-game challenge match in Seoul versus Lee Sedol with 4 - 1 [24] [25] [26]. During The Future of Go Summit from May 23 to 27, 2017 in Wuzhen, China, AlphaGo won a three-game match versus current world No. 1 ranking player Ke Ji. After the Summit, AlphaGo is now retired from competitive play while DeepMind continues AI research in other areas [27].

AlphaGo Zero & AlphaZero

However, in October 2017, AlphaGo Zero, an evolution of AlphaGo was introduced. While previous versions were initially trained on thousands of human amateur and professional games to learn how to play Go, AlphaGo Zero learns exclusively by playing games against itself, starting from completely random play, to quickly surpass human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0 [28] [29]. AlphaGo Zero was further improved and even generalized for other games now dubbed AlphaZero, as published in December 2017 [30].

Fine Art

Fine Art is a Go playing entity developed since 2016 under the patronage of the Chinese media company Tencent by a team around Liu Yongsheng, along with Ma Bo, Tang Shanmin, Wu Guangyu, and Zhang Kaixu. It won the Computer Go UEC Cup at the University of Electro-Communications, Chōfu, Tokyo, Japan, in March 2017 against a field of 27 other programs including DeepZenGo and Crazy Stone [31]. In January 2018, it defeated Ke Jie 9P in 77 moves after giving two stones handicap [32] on Fox Weiqi [33] server [34].


Quote by Gian-Carlo Pascutto in 2010 [35]:

There is no significant difference between an alpha-beta search with heavy LMR  and a static evaluator (current state of the art in chess) and an UCT searcher with a small exploration constant that does playouts (state of the art in go).
The shape of the tree they search is very similar. The main breakthrough in Go the last few years was how to backup an uncertain Monte Carlo score. This was solved. For chess this same problem was solved around the time quiescent search was developed.
Both are producing strong programs and we've proven for both the methods that they scale in strength as hardware speed goes up.
So I would say that we've successfully adopted the simple, brute force methods for chess to Go and they already work without increases in computer speed. The increases will make them progressively stronger though, and with further software tweaks they will eventually surpass humans. 

Computer Olympiads

See also




Videos on Go

Selected Publications


1960 ...

1970 ...

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2000 ...





2005 ...





2010 ...





2015 ...





2020 ...

Forum Posts

2005 ...

Re: A thought about ratings by Don Dailey, Computer Go Archive, December 10, 2007
Re: A thought about ratings by Edward de Grijs, Computer Go Archive, December 10, 2007
Re: A thought about ratings by Don Dailey, Computer Go Archive, December 10, 2007

2010 ...

2015 ...




2020 ...

External Links


Computer Go Archives

Program database - Program list
Program database - Programmer list
Computer Go - Past Events - Acorn 1984 » BBC Micro
1998 Ing Computer Goe Cup - Stories
Human-Computer Go Challenges

Computer Go Pages

Go Servers at Sensei's Library
Counting Legal Positions in Go by John Tromp, January 20, 2016

Open Source


Go Challenge


The computer that mastered Go, with Demis Hassabis and David Silver, YouTube Video

Fine Art


  1. The Age of Intelligent Machines
  2. Albert Zobrist (1970). Feature Extraction and Representation for Pattern Recognition and the Game of Go. Ph.D. thesis , University of Wisconsin, also published as technical report, pdf
  3. Go at the Computer Olympiad
  4. Bernd Brügmann (1993). Monte Carlo Go. pdf
  5. Bruno Bouzy (2005). Associating domain-dependent knowledge and Monte Carlo approaches within a go program. Information Sciences, Heuristic Search and Computer Game Playing IV
  6. Rémi Coulom (2006). Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. CG 2006, pdf
  7. Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning. ECML-06, LNCS/LNAI 4212, pdf
  8. Sylvain Gelly, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Levente Kocsis, David Silver, Csaba Szepesvári (2012). The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions. Communications of the ACM, Vol. 55, No. 3, pdf preprint
  9. Ilya Sutskever, Vinod Nair (2008). Mimicking Go Experts with Convolutional Neural Networks. ICANN 2008, pdf
  10. Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski (1993). Temporal Difference Learning of Position Evaluation in the Game of Go. NIPS 1993
  11. Erik van der Werf, Jos Uiterwijk, Eric Postma, Jaap van den Herik (2002). Local Move Prediction in Go. CG 2002
  12. Markus Enzenberger (2003). Evaluation in Go by a Neural Network using Soft Segmentation. Advances in Computer Games 10, pdf
  13. Convolutional neural network from Wikipedia
  14. Christopher Clark, Amos Storkey (2014). Teaching Deep Convolutional Neural Networks to Play Go. arXiv:1412.3409
  15. Deep learning for… Go by Erik Bernhardsson, December 11, 2014
  16. Teaching Deep Convolutional Neural Networks to Play Go by Hiroshi Yamashita, The Computer-go Archives, December 14, 2014
  17. Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time | MIT Technology Review, December 15, 2014
  18. Teaching Deep Convolutional Neural Networks to Play Go by Michel Van den Bergh, CCC, December 16, 2014
  19. Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver (2014). Move Evaluation in Go Using Deep Convolutional Neural Networks. arXiv:1412.6564v1
  20. Move Evaluation in Go Using Deep Convolutional Neural Networks by Aja Huang, The Computer-go Archives, December 19, 2014
  21. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis (2016). Mastering the game of Go with deep neural networks and tree search. Nature, Vol. 529
  22. Fan Hui at Sensei's Library
  23. Game Over? AlphaGo Beats Pro 5-0 in Major AI Advance « American Go E-Journal, January 27, 2016
  24. DeepMind - YouTube Channel
  25. Video Interview with Rémi Coulom on AlphaGo, February 2016
  26. Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol, BBC News, March 12, 2016
  27. AlphaGo’s Designers Explore New AI After Winning Big in China by Cade Metz, Wired, May 27, 2017
  28. AlphaGo Zero: Learning from scratch by Demis Hassabis and David Silver, DeepMind, October 18, 2017
  29. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis (2017). Mastering the game of Go without human knowledge. Nature, Vol. 550
  30. David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arXiv:1712.01815
  31. Fine Art (software) from Wikipedia
  32. Two stones! Fine Art defeated Ke Jie 9P after giving two stones handicap. – Website of The International Go Federation, January 19, 2018
  33. Go Servers at Sensei's Library - Fox Weiqi
  34. Breakthrough: Fine Art beating Ke Jie with 2 Handicap Stones by Ingo Althöfer, Computer Go Archive, January 20, 2018
  35. Re: Chess vs Go // AI vs IA by Gian-Carlo Pascutto, June 02, 2010
  36. Computer Go Bibliography, University of Alberta
  37. GoTools - TsumeGo Solving Software
  38. Gobble
  39. Nici Schraudolph’s go networks, review by Jay Scott
  40. Mathematical Go from Sensei's Library
  41. EZ-GO at Sensei's Library
  42. Tsumego at Sensei's Library
  43. steganography from Wikipedia
  44. The Shodan Go Bet
  45. Re: Teaching Deep Convolutional Neural Networks to Play Go by Erik van der Werf, The Computer-go Archives, December 15, 2014
  46. Capturing race from Wikipedia
  47. Fuzzy markup language (FML) from Wikipedia
  48. Franz-Josef Dickhut from Wikipedia, Rémi Coulom
  49. codecentric go challenge 2014: Interviews with Franz-Josef Dickhut and Rémi Coulom - codecentric Blog by Raymond Georg Snatzke, October 1, 2014
  50. codecentric go challenge 2014: Final Interviews - codecentric Blog by Raymond Georg Snatzke, November 27, 2014 (German)
  51. How Facebook’s AI Researchers Built a Game-Changing Go Engine | MIT Technology Review, December 04, 2015
  52. Combining Neural Networks and Search techniques (GO) by Michael Babigian, CCC, December 08, 2015
  53. Re: Minmax backup operator for MCTS by Brahim Hamadicharef, CCC, December 30, 2017
  54. AlphaZero: Shedding new light on the grand games of chess, shogi and Go by David Silver, Thomas Hubert, Julian Schrittwieser and Demis Hassabis, DeepMind, December 03, 2018
  55. ELF OpenGo: An Open Reimplementation of AlphaZero by Carl Lumma, CCC, February 13, 2019
  56. GitHub - pytorch/ELF: ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation
  57. KataGo by Warren D. Smith, LCZero Forum, March 16, 2021
  58. New DeepMind paper by GregNeto, CCC, November 21, 2019
  59. MuZero: Mastering Go, chess, shogi and Atari without rules
  60. The Mystery of Go, the Ancient Game That Computers Still Can’t Win by Alan Levinovitz, Wired, May 12, 2014
  61. Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick (2019). ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero- arXiv:1902.04522
  62. Wired Article on Computer GO by Edmund Moshammer, CCC, May 13, 2014
  63. World #1 Go Player Ke Jie accepts Google Alpha Go Match.. by AA Ross, CCC, June 07, 2016
  64. Ke Jie from Wikipedia

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