基于智能算法的六子棋博弈行为选择的应用研究
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摘要
行为是生命体外在的表现形式,是生命体内在智能的外部表象,Tyrell认为:“行为选择就是从一组可能的候选集中选择最适合的行为。”因此,行为选择是生命体智能的高级形式。行为选择问题是人工生命研究领域的一个核心问题,人工生命是人工智能的发展,人工生命作为信息科学、生命科学、系统科学等学科的交叉学科,它不是用分析、解剖生命体的方法来理解生命,而是用综合的方法来理解生命,强调系统性和整体性。此外,计算机博弈过程,本质上就是一个对抗性极强的、智能程度高的博弈行为的选择过程,因此,将基于计算机博弈系统的博弈机器人作为人工生命体,并利用人工智能方法来研究智能系统是可行的,也具有重要研究意义。
模仿人类的博弈行为选择过程,本文将博弈机器人划分为“大脑”、“视觉”、“记忆”、“控制”等4个部分,文章所依托的科研项目的最终目标是构造一个在物理棋盘上与人类对弈的博弈机器人,本文的主要工作是设计“大脑”。论文主要研究了以下4个方面的问题:
第一、设计实现了一套博弈系统,包括棋盘和棋子在计算机中的表示问题,走法生成,搜索技术,估值函数等。
第二、针对基于棋形的六子棋博弈系统中,棋形难以判断和统计的问题,提出并规范了“路”的思想,使对局面进行评估前的准备工作大大简化。
第三、针对静态估值函数依赖人类棋类知识和评估不够准确的问题,本文在前人研究的基础上,对遗传算法的应用进行了改进,克服了原来遗传算法过早收敛的问题,同时提高了种群的多样性,实验结果证明该方法有效。
第四、同样针对静态估值函数依赖人类棋类知识和评估不够准确的问题,提出了采用基于聚焦距离动态调整惯性权重的方法,以改进微粒群算法,以此优化相关参数。实验证明,该算法比遗传算法搜索速度更快,结果更优,是解决评估函数参数优化的有效办法。
Behavior is the manifestation of life in vitro, and it is the external appearance of life’s intelligent. Tyrell said:“Behavior selection is that selecting the most appropriate behavior from a group of potential candidates.”Therefore, behavior selection is an advanced form of intelligent life. Behavior selection is the core issue of artificial life research. Artificial life is the development of artificial intelligence. Artificial life, as an interdisciplinary subject of information science, life science and system science, doesn’t understand life by analyzing and anatomizing, but uses an integrated approach to understand life. It emphasizes on the systematic and holistic. In addition, the process of computer game is essentially a process of highly antagonism and intelligent behavior. Therefore, taking the game robot which based on computer game system as artificial life, and using intelligent methods to research the intelligent systems are feasible and have important research significance.
By mimicking the process of human’s behavior of the game selection, this article divided the game robot into four parts - "brain", "visual", "memory", "control". The ultimate goal of the research project was constructing a game robot which could play game on physical board with human. The main work of this paper is designing the "brains". This article addressed the following four aspects:
First, a game system has been designed in this article. It includes the expression of the board and pieces in the computer move generation, search technology and evaluation functions.
Second, for the problem that it’s difficult to judge and count the chess pieces in the Connect6 game system, the thinking of“path”has been proposed and standardized. It makes the preparatory work before evaluating the situation greatly simplified.
Third, for the problem that the static evaluation function relying on human’s chess knowledge and it is not accurate enough, based on previous studies, the application of genetic algorithms has been improved. It overcomes the original problem of premature convergence of genetic algorithm, and it increases the diversity of population. The experimental results show that the method is effective.
Fourth, for the problem that the static evaluation function relying on human’s chess knowledge and it is not accurate enough, a new adaptive Particle Swarm Optimization algorithm which dynamically changes inertia weight based on the current rate of cluster focus distance was proposed to improve the PSO algorithm and optimize parameters. Experiments show that the algorithm is faster than the genetic algorithm and the result is better than genetic algorithms. It is an effective way to solve the optimization of the parameters of evaluation function.
模仿人类的博弈行为选择过程,本文将博弈机器人划分为“大脑”、“视觉”、“记忆”、“控制”等4个部分,文章所依托的科研项目的最终目标是构造一个在物理棋盘上与人类对弈的博弈机器人,本文的主要工作是设计“大脑”。论文主要研究了以下4个方面的问题:
第一、设计实现了一套博弈系统,包括棋盘和棋子在计算机中的表示问题,走法生成,搜索技术,估值函数等。
第二、针对基于棋形的六子棋博弈系统中,棋形难以判断和统计的问题,提出并规范了“路”的思想,使对局面进行评估前的准备工作大大简化。
第三、针对静态估值函数依赖人类棋类知识和评估不够准确的问题,本文在前人研究的基础上,对遗传算法的应用进行了改进,克服了原来遗传算法过早收敛的问题,同时提高了种群的多样性,实验结果证明该方法有效。
第四、同样针对静态估值函数依赖人类棋类知识和评估不够准确的问题,提出了采用基于聚焦距离动态调整惯性权重的方法,以改进微粒群算法,以此优化相关参数。实验证明,该算法比遗传算法搜索速度更快,结果更优,是解决评估函数参数优化的有效办法。
Behavior is the manifestation of life in vitro, and it is the external appearance of life’s intelligent. Tyrell said:“Behavior selection is that selecting the most appropriate behavior from a group of potential candidates.”Therefore, behavior selection is an advanced form of intelligent life. Behavior selection is the core issue of artificial life research. Artificial life is the development of artificial intelligence. Artificial life, as an interdisciplinary subject of information science, life science and system science, doesn’t understand life by analyzing and anatomizing, but uses an integrated approach to understand life. It emphasizes on the systematic and holistic. In addition, the process of computer game is essentially a process of highly antagonism and intelligent behavior. Therefore, taking the game robot which based on computer game system as artificial life, and using intelligent methods to research the intelligent systems are feasible and have important research significance.
By mimicking the process of human’s behavior of the game selection, this article divided the game robot into four parts - "brain", "visual", "memory", "control". The ultimate goal of the research project was constructing a game robot which could play game on physical board with human. The main work of this paper is designing the "brains". This article addressed the following four aspects:
First, a game system has been designed in this article. It includes the expression of the board and pieces in the computer move generation, search technology and evaluation functions.
Second, for the problem that it’s difficult to judge and count the chess pieces in the Connect6 game system, the thinking of“path”has been proposed and standardized. It makes the preparatory work before evaluating the situation greatly simplified.
Third, for the problem that the static evaluation function relying on human’s chess knowledge and it is not accurate enough, based on previous studies, the application of genetic algorithms has been improved. It overcomes the original problem of premature convergence of genetic algorithm, and it increases the diversity of population. The experimental results show that the method is effective.
Fourth, for the problem that the static evaluation function relying on human’s chess knowledge and it is not accurate enough, a new adaptive Particle Swarm Optimization algorithm which dynamically changes inertia weight based on the current rate of cluster focus distance was proposed to improve the PSO algorithm and optimize parameters. Experiments show that the algorithm is faster than the genetic algorithm and the result is better than genetic algorithms. It is an effective way to solve the optimization of the parameters of evaluation function.
引文
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[3]蔡自兴,徐光佑.人工智能及其应用[M].北京:清华大学出版社, 2003.
[4]罗伯特?吉本斯.博弈论基础[M].北京:中国社会科学出版社, 1999.
[5] Frederic Friedel, michael译注.电脑国际象棋简史[EB/OL]. http://www.chessit.net/ file_topic/computerchess/c_briefhistory.htm.
[6]邵桂芳.基于人工生命方法的自主智能体行为选择研究[D].重庆:重庆大学
[7] Seiji Yamada. Adaptive Action Selection without Explicit Communication for Multi-robot Box-pushing[A]. IEEE/RSJ International Conference on Intelligent Robots and Systems[C]. 1999.
[8]谢汝林,李祖枢.2003.基于优先度的人工生命体行为选择研究[J],广西师范大学学报,21(1):1-6.
[9]李祖枢,谢汝林,张小川,邵桂芳.2005.人工生命体行为选择及其进化研究[J],模式识别与人工智能,18(3).
[10]李祖枢.2005.基于图式理论的人工生命体行为选择体系结构研究(特邀报告)[J],第二届人工生命及应用专题学术会议论文集.
[11] Guifang Shao,Zushu Li.2008.The behavior selection of artificial life based on evaluation-tradoff structure[J], proceedings of the 7th world congress on intelligent control and automation,820-824.
[12] David N. L. Levy, eds. Computer Games[J]. New York: Springer New York Inc, 1998, 25(3): 335-365.
[13] Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor[J], MI: The University of Michigan Press, 1975.
[14] Mchelewicz Z. Genetic Algorithms + Data Structure = Evolutionary Programming[J]. Springer - Verlag, 1996.
[15] Fogel L J, et al. Artificial Intelligence through Simulation Evolution[J]. Chichester: John Wiley, 1966.
[16] Fogel D B. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence[J]. IEEE Press, 1995.
[17] Koza J R. Genetic Programming: On the Programming of Computers by Means of Netural Selection[J]. Cambridge, MA: MIT Press,1992.
[18] Dorigo M, Stutzle T. Ant Colony Optimization[J]. The MIT Press. Cambridge, Masschusetts London, England, 2004.
[19] Kennedy J, Eberhart K. Particle Swarm Optimization[C]. In: Proc IEEE Int Conference on Neural Networks, 1995,1942-1948.
[20] David j. Kruglinski, Scot Wingo & George Shepherd. Programming Microsoft Visual C++.5th Edition[M], Microsoft Press, 1999.
[21] Marsland T A.Computer chess and search[D].Edmonton:University of Alberta,1991.
[22]王小春. PC游戏编程[M].重庆:重庆大学出版社, 2002.1–27.
[23] David j. Kruglinski, Scot Wingo & George Shepherd. Programming Microsoft Visual C++.5th Edition[M], Microsoft Press, 1999.
[24] J.Schaeffer, Distributed Game-tree Searching, Journal of Parallel and Distributed Computing, Vol.6,1989.
[25] A.Plaat, J.Schaeffer, W.Pijls, A.de Bruin, Best-first Fixed-depth Minimax Algorithms[J]. Aritificial Intelligence. 1996.
[26] Rivest, R.L.Intelligence, Game Tree Searching by MinMax Approximation[J]. Artificial 1998, Vol.34, No.1
[27] Shannon. Claude E Programming a computer for playing chess [J]. Philosophical Magazine Vol. 41 1950 256-275.
[28] TODHUN TER I. A History of the Mathematical Theory of Probability from the Time of Passcal to that of Lap lace[M] New York: Chelsea Publishing, 1965: 105-108.
[29] BOREL E. The theory of play and integral equations with skew symmetric kernels[J]. Econometrica, 1953, 21: 91-117.
[30] Rivest, R-L Game Tree Searching by MinMax Approximation [J]. Artificial Intelligence, 1998, Vol.34, No.1
[31] Donald E, Knuth and Ronald W, Moore. An Analysis of Alpha-Beta Pruning[J]. Artificial Intelligence, vol.6, 1975:293-326.
[32]陆汝铃.人工智能(上册)[M].第一版.北京:科学出版社, 1989.8
[33]林尧瑞,马少平.人工智能导论[M].1989北京清华大学出版社.
[34] Francis Dominic Laram.博弈编程指南[EB/OL].http://www.gamedev.net.
[35] Herik,H.J.van den,and Allis,L.V.(eds.)(1992).Heuristic Programming in Artificial Intelligence 3:the third computer Olympiad[M]. Ellis Horwood Ltd.,Chichester,England.
[36] Yen SJ, Chen J C, Yang T N. Computer Chinese Chess[J]. ICGA Journal, 2004, (3): 3-18.
[37]张小川,舒良等,关于六子棋计算机博弈策略的探讨[J].中国人工智能进展(2007),2007.12
[38]肖齐英,王正志.博弈树搜索与静态估值函数闭[J],计算机应用研究, 1997.
[39]计算机博弈[EB/OL]. http://www.elephantbase.net/computer.htm
[40]王凌.智能优化算法及其应用[M],清华大学出版社, 2001.
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