Hosts

IEEE SMC

IEEE SMC 2017

NUTN, Taiwan
Co-sponsors

HeroIT.com Co. Ltd.

 
 
Human vs. Computer Go Competitions @ CIS Flagship Conferences

Conference
- FUZZ-IEEE 2009
- IEEE WCCI 2010
- IEEE SSCI 2011
- FUZZ-IEEE 2011
- IEEE WCCI 2012
- FUZZ-IEEE 2013
- FUZZ-IEEE 2015
- IEEE CIG 2015
- IEEE WCCI 2016
- FUZZ-IEEE 2017

Human vs. Computer Go Competitions

Held Activities
- 2008 Computational Intelligence Forum & World 9x9 Computer Go Championship
- Taiwan Open 2009
- TAAI 2012
- TAAI 2015
- ICIRA 2016
- MoGoTW

Background and Impact

Since 2009, the IEEE Computational Intelligence Society (CIS) has funded human vs. computer Go competitions in flagship conferences, including FUZZ-IEEE 2009, IEEE WCCI 2010, IEEE SSCI 2011, FUZZ-IEEE 2011, IEEE WCCI 2012, FUZZ-IEEE 2013, FUZZ-IEEE 2015, IEEE CIG 2015, and IEEE WCCI 2016. The technique of Monte Carlo Tree Search (MCTS) has revolutionized the field of computer game-playing for the past decade. However, the progress of computer Go has been bounded to four handicaps against with top professional Go players since about 2012-2015. Google DeepMind introduced a new approach to computer Go that combines Monte Carlo tree search (MCTS) with deep learning including value and policy networks in Oct. 2015. Honored with a 9-Dan professional award by Korea Baduk Association, AlphaGo made a great milestone for the development on the computer Go and artificial intelligence (AI) in Mar. 2016. In addition, Google Master won the 60 games on the Internet in Jan. 2017.

Besides, the FUZZ-IEEE 2009: Panel, Invited Sessions, and Human vs. Computer Go Competition was held at the 2009 International Conference on Fuzzy Systems in Aug. 2009. This event was the first human vs. computer Go competition hosted by the IEEE Computational Intelligence Society (CIS) at the IEEE CIS flag conference. In 2010, MoGo and Many Faces of Go achieved wins against strong amateur players on 13x13 with only two handicap stones. In April 2011, MoGoTW broke a new world record by winning the first 13x13 game against the 5 Dan professional Go player with handicap 3 and reversed komi of 3.5. It also won 3 out of 4 games of Blind Go in 9x9. In June 2011, in the three-day completion held at FUZZ-IEEE 2011, there are four programs, including MoGoTW, Many Faces of Go, Fuego, and Zen, invited to join this competition, and more than ten invited professional Go players accept the challenge, including Chun-Hsun Chou (9P), Ping-Chiang Chou (5P), Joanne Missingham (5P), and Kai-Hsin Chang (4P). The computer Go program Zen from Japan won each competition even playing 19x19 game with Chun-Hsun Chou (9P) with handicap 6, showing that the level of computer Go programs in 19x19 game is estimated at 4D. In addition, MoGoTW also won all of twenty 7x7 games under a specific komi, that is, setting komi 9.5 and 8.5 as MoGoTW is White and Black, respectively, suggesting that the perfect play is a draw with komi 9. MoGoTW with the adaptive learning ability was first played with the amateur Go players from kyu level to dan level in Taiwan, on May 6 and May 27, 2012. Estimating the level of an opponent is useful for choosing the right strength of an opponent and for attributing relevant ranks to players. We estimate the relation between the strength of a player and the number of simulations needed for a MCTS to have the same strength. In addition, we also play against many players in the same time, and try to estimate their strength. In Oct. 2015, AlphaGo and Fan Hui (2P) competed in a formal five-game match and AlphaGo won the match 5 games to 0. In Mar. 2016, AlphaGo won four of the five 19×19 games without handicap against Lee Sedol (9P). And, commentators noted that AlphaGo played many unprecedented, creative, and even beautiful moves. Zen, Facebook Darkforest, and Crazy Stone got the first three places in the 9th Computer Go UEC Cup.

Facebook Darkforest Go engine powered by deep learning has been developed mainly by Dr. Yuandong Tian from Facebook AI Research (FAIR) since May 2015 and was open to the public in 2016. Darkforest Go has a stable rank of 5D on the KGS server and pure policy network achieves a stable rank of 3D on KGS. It received the third place and second place in the KGS Go Tournament 2016 and in the ninth UEC Cup Computer 2016, respectively. The IEEE WCCI 2016 in Canada and ICIRA 2016 in Japan also invited Darkforest Go to join the demo games with top professional Go players.

Reference
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[2]
C. S. Lee, M. Mueller, and O. Teytaud, “Special Issue on Monte Carlo Techniques and Computer Go”, IEEE Transactions on Computational Intelligence and AI in Games, vol. 2, no. 4, pp. 225-228. Dec. 2010.
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[19] C. S. Lee, M. H. Wang, S. J. Yen, T. H. Wei, I. C. Wu, P. C. Chou, C. H. Chou, M. W. Wang, and T. H. Yang, "Human vs. computer Go: review and prospect," IEEE Computational Intelligence Magazine, vol. 11, no. 3, pp. 67-72, Aug. 2016.
[20]Y. Tian and Y. Zhu, “Better computer Go player with neural network and long-term prediction,” Jan. 2016, [Online] Available: https://arxiv.org/abs/1511.06410.
[21] G. Acampora, B. D. Stefano, and A. Vitiello, “IEEE 1855TM: The first IEEE standard sponsored by IEEE Computational Intelligence Society,” IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 4–6, Nov. 2016.
[22] E. Gibney, “Google secretly tested AI bot: updated version of Google DeepMind’s AlphaGo program revealed as mystery online player,” Nature, vol. 541, pp. 142, Jan. 2017.
[23] E. Gibney, “Google masters Go: deep-learning software excels at complex ancient board game,” Nature, vol. 529, pp. 446, Jan. 2016.
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[26] C. S. Lee, M. H. Wang, C. H. Kao, S. C. Yang, Y. Nojima, R. Saga, N. Shuo, N. Kubota, “FML-based Prediction Agent and Its Application to Game of Go,” Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Japan, Jun. 27-30, 2017.



         
Co-organizers

KWS, NUTN

CCS, Japan

MOST, Taiwan

HAMASTAR

NCHC

Hifong Weiqi Academy

Bureau of Education

Bureau of Education

KGS
 
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