• Website

http://fuzzieee2011.nutn.edu.tw/go/ or http://www.fuzz-ieee2011.org/go/

 

  • 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.

 

  • Date

June 27-June 29, 2011

 

  • Place

Magpie Room, Grand Hyatt Taipei

 

  • 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)

  Shao-Chieh Ting (2P)

  Chun-Yen Lin (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)

 

  • Organizers

  IEEE / IEEE Computational Intelligence Society (CIS)

  National University of Tainan (NUTN), Taiwan

  INRIA, France

  National Science Council (NSC), Taiwan

  Grid 5000 Project, France

  Taiwanese Association for Artificial Intelligence (TAAI), Taiwan

  Taiwan Association of Cloud Computing (TACC), Taiwan

 

  • Co-Chairs

 Chang-Shing Lee, National University of Tainan, Taiwan

  Martin Mueller, University of Alberta, Canada

  Olivier Teytaud, TAO-Inria, France

Shun-Chin Hsu, Chang Jung Christian University, Taiwan

 

  • Potential List of Panelists: (tentative)

  Chang-Shing Lee, National University of Tainan, Taiwan

  Martin Mueller, University of Alberta, Canada

  Wen-Yang Lin, National University of Kaohsiung, Taiwan

  Olivier Teytaud, TAO-Inria, France

  Kuo-Ning Chiang, National Center for High-Performance Computing, Taiwan

  Hani Hagras, University of Essex, UK

  Tzung-Pei Hong, National University of Kaohsiung, Taiwan

  Vincenzo Loia, University of Salerno, Italy

  Shi-Jim Yen, National Dong Hwa University, Taiwan

  Gary Yen, Oklahoma State University, USA

  Shun-Chin Hsu, Chang Jung Christian University, Taiwan

  Piero Bonissone, GE Global Research, USA

  Shang-Rong Tsai, Chang Jung Christian University, Taiwan

  Giovanni Acampora, University of Salerno, Italy

  Yeh-Ching Chung, National TsingHua University, Taiwan

  Fabien Teytaud, TAO-Inria, France

  Chun-Nan Hsu, Academia Sinica, Taiwan

  Yau-Hwang Kuo, National Cheng-Kung University, Taiwan

 

  • Related Website

  http://fuzzieee2011.nutn.edu.tw/go/

  http://mogotw.nutn.edu.tw

  http://ssci2011.nutn.edu.tw

  http://wcci2010.nutn.edu.tw

  http://oase.nutn.edu.tw/FUZZ_IEEE_2009/

  http://go.nutn.edu.tw/2009/

  http://go.nutn.edu.tw/

 

  • 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.