The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

Chang-Shing Lee*, Mei-Hui Wang, Guillaume Chaslot, Jean-Baptiste Hoock, Arpad Rimmel, Olivier Teytaud, Shang-Rong Tsai, Shun-Chin Hsu, Tzung-Pei Hong

*Corresponding author for this work

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    Abstract

    In order to promote computer Go and stimulate further development and research in the field, the event activities, Computational Intelligence Forum and World 9 x 9 Computer Go Championship, were held in Taiwan. This study focuses on the invited games played in the tournament Taiwanese Go Players Versus the Computer Program MoGo held at the National University of Tainan (NUTN), Tainan, Taiwan. Several Taiwanese Go players, including one 9-Dan (9D) professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines all-moves-as-first (AMAF)/rapid action value estimation (RAVE) values, online "upper confidence tree (UCT)-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: 1) the weakness in corners, 2) the scaling over time, 3) the behavior in handicap games, and 4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan (3D) with: 1) good skills for fights, 2) weaknesses in corners, in particular, for "semeai" situations, and 3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in AI and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer Chess or Chinese Chess in the future.

    Original languageEnglish
    Pages (from-to)73-89
    Number of pages17
    JournalIEEE Transactions on Computational Intelligence and AI in Games
    Volume1
    Issue number1
    DOIs
    Publication statusPublished - Mar 2009

    Keywords

    • Computational intelligence
    • computer Go
    • game
    • MoGo
    • Monte Carlo tree search (MCTS)

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