-Program
-Computer Go Competition
  -June 24, 2011
  -June 25, 2011
  -June 26, 2011
  -June 27, 2011
  -June 28, 2011
  -June 29, 2011
-Forum
  -June 26, 2011
-Workshop
  -June 27, 2011
 
Hosts

IEEE CIS

FUZZ-IEEE 2011

NUTN, Taiwan

TAO, INRIA, France
 
 
Co-sponsors

HeroIT.com Co. Ltd.

Alcatel

Chinese Association for Go, Taiwan

HP

STI

 
 
Description

    The technique of Monte Carlo Tree Search (MCTS) has revolutionized the field of computer game-playing, and is starting to have an impact in other search and optimization domains as well. In past decades, the dominant paradigm in game algorithms was alpha-beta search. This technique, with many refinements and game-specific engineering, lead to breakthrough performances in classic board games such as chess, checkers and Othello. After Deep Blue’s famous victory over Kasparov in 1996, some of the research focus shifted to games where alpha-beta search was not sufficient. Most prominent among these games was the ancient Asian game of Go. During the last few years, the use of MCTS techniques in Computer Go has really taken off, but the groundwork was laid much earlier. In 1990, Abramson [1] proposed to model the expected outcome of a game by averaging the results of many random games. In 1993, Bruegmann [2] proposed Monte-Carlo techniques for Go using almost random games, and developed the refinement he termed all-moves-as-first (AMAF). Ten years later, a group of French researchers working with Bruno Bouzy took up the idea [3]. Bouzy’s Indigo program used Monte-Carlo simulation to decide between the top moves proposed by a classical knowledge-based Go engine [4]. RemiCoulom’s Crazy Stone [5] was the first to add the crucial second element, a selective game tree search controlled by the results of the simulations. he last piece of the puzzle was the Upper-Confidence Tree (UCT) algorithm of Kocsis and Szepesvari [6], which applied ideas from the theory of multi-armed bandits to the problem of how to selectively grow a game tree. Gelly and Wang developed the first version of MoGo [7], which among other innovations combined Coulom’s ideas, the UCT algorithm, and pattern-directed simulations. AMAF was revived and extended in Gelly and Silver’s Rapid Action Value Estimate (RAVE), which computes AMAF statistics in all nodes of the UCT tree. Rapid progress in applying knowledge and parallelizing the search followed. Today, programs such as MoGo/MoGoTW, Crazy Stone, Fuego, Many Faces of Go, and Zen have achieved a level of play that seemed unthinkable only a decade ago. These programs are now competitive at a professional level for 9 9 Go and amateur Dan strength on 19 19 [4], [8].

    One measure of success is competitions. In Go, Monte-Carlo programs now completely dominate classical programs on all board sizes (though no one has tried boards larger than 19 19). Monte-Carlo programs have achieved considerable success in play against humans. An early sign of things to come was a series of games on a 7 7 board between Crazy Stone and professional 5th Dan Guo Juan. Crazy Stone demonstrated almost perfect play. Since 2008, National University of Tainan (NUTN) in Taiwan and other academic organizations have hosted or organized several human vs. computer Go-related events, including the 2008 Computational Intelligence Forum & World 9 9 Computer Go Championship [8], and 2009 Invited Games for MoGo vs. Taiwan Professional Go Players (Taiwan Open 2009) [9]. Besides, the FUZZ-IEEE 2009: Panel, Invited Sessions, and Human vs. Computer Go Competition [10] 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 13 13 with only two handicap stones. On the full 19 19 board, programs have racked up a number of wins (but still a lot more losses) on 6 and 7 handicap stones against top professional Go players [11], [12]. Also, computer Go Programs have won both as White and Black against top players in 9 9 game [13]. Additionally, in April 2011, MoGoTW broke a new world record by winning the first 13 13 game against the 5th 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 9 9.
 
Human

More than 10 Taiwanese Professional Go Players will join this competition
- Chun-Hsun Chou (9P)
- Ping-Chiang Chou (5P)
- Joanne Missingham (5P)
- Hsiang-Jen Huang (5P)
-
Kai-Hsin Chang (4P)
-
Yin-Nan Chou (4P)
-
Yu-Hsiang Lin (4P)
-
Hsiu-Ping Lin (3P)
- Cheng-Jui Yu (3P)
-
Shih Chin (2P)
-
Chun-Yen Lin (2P)
-
Shao-Chieh Ting (2P)
-
Yu-Pang Kou (1P)
- Hsiang-Chieh Wang (1P)
-
Ting-Yi Lin (1P)

 
Computer Go Program
- MoGo/MoGoTW (France / Taiwan)
- Fuego (Canada)
- Many Faces of Go (USA)
- Zen (Japan)
 
Reference
[1] B. Abramson, Expected-outcome: a general model of static evaluation, IEEE
     Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 2,
     pp. 182-193, 1990.
[2] B. Brugmann, Monte Carlo Go, 1993. Online at  
     http://www.ideanest.com/vegos/MonteCarloGo.pdf.
[3] B. Bouzy and T. Cazenave, “Computer Go: an AI-oriented survey,” Artificial
     Intelligence Journal, vol. 132, no. 1, pp. 39-103, 2001.
[4] 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.
[5] R. Coulom, “Efficient selectivity and backup operators in Monte-Carlo tree
     search,” in Proceeding of 5th International Conference on Computers and
     Games, Turin, Italy, 2006, pp. 72–83.
[6] L. Kocsis and C. Szepesvari, “Bandit based Monte-Carlo planning,” Machine
     Learning ECML, vol. 4212, pp. 282-293, Springer, 2006.
[7] Y. Wang and S. Gelly, “Modifications of UCT and sequence-like simulations
     for Monte-Carlo Go,” in Proceedings of the 2007 IEEE Symposium on
     Computational Intelligence and Games (CIG07), Hawaii, USA, 2007, pp. 175-
     182.
[8] C. S. Lee, M. H. Wang, C Chaslot, J. B. Hoock, A. Rimmel, O. Teytaud, S.
     R. Tsai, S. C. Hsu, and T. P. Hong, “The computational intelligence of MoGo
     revealed in Taiwan's computer Go tournaments,” IEEE Transactions on
     Computational Intelligence and AI in Games, vol. 1, no. 1, pp. 73-89, Mar.
     2009.
[9] C. S. Lee, M. H. Wang, T. P. Hong, G. Chaslot, J. B. Hoock, A. Rimmel, O.
     Teytaud, and Y. H. Kuo, "A novel ontology for computer Go knowledge
     management," in Proceeding of the 2009 IEEE International Conference on
     Fuzzy Systems (FUZZ-IEEE 2009), Jeju Island, Korea, Aug. 19-14, 2009, pp.
     1056–1061.
[10] S. J. Yen, C. S. Lee, and O. Teytaud, “Human vs. computer Go competition
       in FUZZ-IEEE 2009,” International Computer Games Association, vol. 32,
       no. 3, pp. 178–180, Sept. 2009.
[11] J. B. Hoock, C. S. Lee, A. Rimmel, F. Teytaud, M. H. Wang, and O.
      Teytaud, “Intelligent agents for the game of Go,” IEEE Computational
      Intelligence Magazine, vol. 5, no. 4, pp. 28-42, Nov. 2010.
[12] C. S. Lee, M. H. Wang, O. Teytaud, and Y. L. Wang, “The game of Go @
       IEEE WCCI 2010,” IEEE Computational Intelligence Magazine, vol. 5, no. 4,
       pp. 6-7, Nov. 2010.
[13] M. H. Wang, C. S. Lee, Y. L. Wang, M. C. Cheng, O. Teytaud, and S. J.
       Yen, “The 2010 contest: MoGoTW vs. human Go players,” International
       Computer Games Association, vol. 33, no. 1, pp. 47-50, Mar. 2010.





Co-organizers

TAAI, Taiwan

NSC, Taiwan

TACC, Taiwan

University of Alberta, Canada

Computer Center of NUTN 

CSIE of NUTN 

KWS of NUTN

KGS 

Grid'5000
 
CJCU, Taiwan

NDHU, Taiwan 

NUK, Taiwan
 
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