【作者】 邢胜；

【导师】 王熙照； 翟俊海；

【作者基本信息】 河北大学 ， 计算机软件与理论， 2009， 硕士

【摘要】 中国象棋的计算机博弈研究起步较晚,但是发展较快。到目前为止出现了许多优秀的中国象棋软件,如许舜钦及其团队的“ELP”、上海计算机博弈研究所黄晨的“象眼”等。但是这些象棋软件大多数是通过优化数据结构和改进搜索策略等方法提高棋力,虽然也具有较高的博弈水平,但是有自学习能力的却很少。本文通过引入数据库存储计算机判断失误的对手着法和局面值,从而使象棋软件能够转变策略,避免再次的判断失误,实现自学习能力。象棋博弈树搜索是中国象棋计算机博弈的关键技术之一。但是博弈树的搜索在没有记忆的情况下不能实现学习,而记忆要借助数据库来实现。付强在其论文中应用了数据库记录计算机所走的最好着法和其局面值,并使用加强学习修改局面值以达到学习的目的。但是在象棋软件输棋的情况下,主要是由于对对手走棋判断失误造成的,所以记录那些与计算机预想不同的对手着法和展开博弈树后的返回值,再从中选择造成输棋结果的关键着法和值。当下次搜索到相同着法时,将数据库中该着法下的值取出,继续搜索使象棋软件实现策略的转变,达到自学习的目的。更多还原

【Abstract】 The research of Chinese chess computer game starts later than chess, but develops quickly. So far there are many excellent Chinese chess programs, such as the "ELP" developed by Shun-Chih Hsu and his team, the "Elephant eye" developed by Chen Huang and so on. Most of the existing Chinese chess programs are improved through optimizing the data structure and improving the search strategy. Self-learning programs have rarely appeared.This thesis proposes a method that can change strategy and avoid making the same mistakes through searching the database with which it stores the’opponent movement computer has misjudged and the position value. In other words, it has the ability of self-learning.Game-tree search is one of the key technologies in Chinese Chess computer game. Game-tree search can achieve self-learning with a database. Fu qiang produced a method which used database to record the best movement and position value, then amended the position value by reinforcement learning to achieve the purpose of study.That Chinese Chess software lost game is mainly because of the opponent movement computer has misjudged. In this thesis, the opponent movements which are different from what computer forecasts and the return value of game-tree are recorded and the key movement and the value that cause failure are chosen. When meeting the same movement, it will read the value of the movement and continue to search. It will change the strategy so that it achieves the ability of self-learning.更多还原