通用对弈游戏:一个探索机器游戏智能的领域
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General Game Playing:A Research Field for Exploring Machine Intelligence in Games
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    摘要:

    通用对弈游戏(general game playing,简称GGP)是致力于提高机器的通用游戏智能的研究领域.与专用游戏智能程序不同,GGP玩家直到游戏开始时才获得游戏规则,从而避免依赖于人类关于特定游戏的经验.GGP研究发展至今,已在游戏表示、搜索算法、状态估值等方面做了深入探索,并在知识迁移等方面做出了尝试.GGP研究的进展在一定程度上代表了通用人工智能的发展,因而是值得关注的.

    Abstract:

    General game playing (GGP) is a research field for improving the general gaming intelligence of machines. It is different from specific game playing in that GGP players do not know game rules before the game begins, which makes them independent from human experience on specific games. Until now, GGP researchers have deeply explored many problems, such as game representation, search algorithm, and state evaluation. Also, some efforts have been made on knowledge transfer. The progress of GGP research represents the development of artificial general intelligence to some extent, which makes it remarkable.

    参考文献
    [1] Genesereth M, Love N, Pell B. General game playing:Overview of the AAAI competition. AI magazine, 2005,26(2):62-72.[doi:10.1609/aimag.v26i2.1813]
    [2] Allis LV. Searching for Solutions in games and artificial intelligence[Ph.D. Thesis]. Maastricht:the Netherlands:University of Limburg, 1994.
    [3] Love N, Hinrichs T, Haley D, Schkufza E, Genesereth M. General game playing:Game description language specification. Stanford:Stanford University, 2008.
    [4] Schiffel S, Thielscher M. A multiagent semantics for the game description language. In:Duval B, van den Herik J, Loiseau S, Filipe J, eds. Proc. of the Agents and Artificial Intelligence. Berlin, Heidelberg:Springer-Verlag, 2010. 44-55.[doi:10.1007/978-3-642-11819-7_4]
    [5] Thielscher M. A general game description language for incomplete information games. In:Proc. of the 24th AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2010. 994-999.
    [6] Clune J. Heuristic evaluation functions for general game playing. In:Proc. of the 22nd AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2007. 1134-1139.
    [7] Schiffel S, Thielscher M. Fluxplayer:A successful general game player. In:Proc. of the 22nd AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2007. 1191-1196.
    [8] Finnsson H, Björnsson Y. Simulation-Based approach to general game playing. In:Proc. of the 23rd AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2008. 259-264.
    [9] Méhat J, Cazenave T. A parallel general game player. Künstliche Intelligenz, 2011,25(1):43-47.[doi:10.1007/s13218-010-0083-6]
    [10] Levine J, Congdon CB, Ebner M, Kendall G, Lucas SM, Miikkulainen R, Schaul T, Thompson T. General video game playing. Artificial and Computational Intelligence in Games, 2013,6:77-83.[doi:10.4230/DFU.Vol6.12191.77]
    [11] Bellemare MG, Naddaf Y, Veness J, Bowling M. The arcade learning environment:An evaluation platform for general agents. Journal of Artificial Intelligence Research, 2013,47:253-279.
    [12] Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D. Human-Level control through deep reinforcement learning. Nature, 2015,518(7540):529-533.[doi:10.1038/nature14236]
    [13] Neumann JV. Zur theorie der gesellschaftsspiele. Mathematische Annalen, 1928,100(1):295-320.[doi:10.1007/BF01448847]
    [14] Cox E, Schkufza E, Madsen R, Genesereth M. Factoring general games using propositional automata. In:Proc. of the IJCAI Workshop on General Intelligence in Game-Playing Agents (GIGA). Menlo Park:AAAI Press, 2009. 13-20.
    [15] Kuhlmann GJ. Automated domain analysis and transfer learning in general game playing[Ph.D. Thesis]. Austin:The University of Texas at Austin, 2010.
    [16] Haufe S, Schiffel S, Thielscher M. Automated verification of state sequence invariants in general game playing. Artificial Intelligence, 2012,187:1-30.[doi:10.1016/j.artint.2012.04.003]
    [17] Kuhlmann G, Dresner K, Stone P. Automatic heuristic construction in a complete general game player. In:Proc. of the 21st AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2006. 1457-1462.
    [18] Browne CB, Powley E, Whitehouse D, Lucas SM, Cowling P, Rohlfshagen P, Tavener S, Perez D, Samothrakis S, Colton S. A survey of Monte Carlo tree search methods. IEEE Trans. on Computational Intelligence and AI in Games, 2012,4(1):1-43.[doi:10. 1109/TCIAIG.2012.2186810]
    [19] Chaslot G, Bakkes S, Szita I, Spronck P. Monte-Carlo tree search:A new framework for game AI., 2008. In:Proc. of the 4th Artificial Intelligence and Interactive Digital Entertainment Conf. AAAI Press, 2008. 216-217.
    [20] Auer P. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 2002,3:397-422.
    [21] Björnsson Y, Finnsson H. Cadiaplayer:A simulation-based general game player. IEEE Trans. on Computational Intelligence and AI in Games, 2009,1(1):4-15.[doi:10.1109/TCIAIG.2009.2018702]
    [22] Finnsson H, Björnsson Y. Learning simulation control in general game-playing agents. In:Proc. of the 24th AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2010. 954-959.
    [23] Schiffel S. Symmetry detection in general game playing. In:Proc. of the 24th AAAI Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2010. 980-985.
    [24] Pell B. Strategy generation and evaluation for meta-game playing[Ph.D. Thesis]. Cambridge:University of Cambridge, 1993.
    [25] Banerjee B, Stone P. General game learning using knowledge transfer. In:Proc. of the 20th Int'l Joint Conf. on Artificial Intelligence. Menlo Park:AAAI Press, 2007. 672-677.
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张海峰,刘当一,李文新.通用对弈游戏:一个探索机器游戏智能的领域.软件学报,2016,27(11):2814-2827

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  • 收稿日期:2015-06-23
  • 最后修改日期:2015-07-17
  • 在线发布日期: 2015-11-17
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