对称无关的模式编码方法在定式学习中的应用

计算机围棋中的定式可以从棋谱中学习，采用与模式对称性无关的编码方法可以取得更好的储存效果，这是一种基于模式的邻近特征、轮廓特征和行列特征的模式编码方法，与模式的平移、旋转、翻转及黑白对称性无关，实验结果表明其哈希性能良好，模式间的查找迅速，也可以在官子和死活问题中得以应用。     

基于C 的围棋定式管理系统

围棋有五种基本定式,将围棋定式与计算机技术结合以后,查询定式、学习定式将不再是麻烦事。文章介绍了基于C 技术的围棋定式管理系统制作原理并详细说明了制作该系统所用到的基本算法。     

彩光模拟围棋微机记录盘

阐述了一种记录围棋棋谱的工具。将彩光模拟输入盘与微机系统相连，以嵌装其上的可发出彩光的模拟棋子模仿围棋实战进程，同时将着手之坐标输入微机内存，并可随时打印输出。此彩光模拟输入盘亦可兼作一种模拟围棋的用具，供对弈、打谱使用。     

基于局面的围棋棋谱检索算法

随着计算机技术在围棋运动中的广泛应用,围棋棋谱的检索也成为围棋信息化的重要需求。传统的棋谱检索不能反映着法的内容,在应用上受到很大限制。介绍一种基于局面的棋谱检索算法,通过对棋子位置建立索引,将搜索直接建立在棋局内容的基础上。算法具有占用存储空间小、检索速度快、准确率高的特点,具有较高的实用价值。     

彩光模拟围棋微机记录盘

众所周知，高等级的围棋比赛需要记录实战的棋谱，而现行的记谱工作仍然是以传统的纸、笔手工方式完成的，即由记录员在纵、横各１９格的记录纸上需要记录的位置用模板画一圆圈，再于圆圈内标上代表着手工序数的数字，由此完成一个实战着手的记录。此种记谱方式比较繁琐，在着手较多以后容易出错，且在实战进程较快时未免使记录员手忙脚乱。由于围棋的规则是落子后不能再行移动，这就给利用微机技术记录棋谱带来了便利条件。本文试图阐述一种新的记谱方式，其中主要部件是一个类似围棋棋盘的彩光模拟输入盘，记谱时只需在此盘上将围棋实战进…     

采用时间差分算法的九路围棋机器博弈系统

围棋机器博弈是机器博弈中重要的分支之一,其庞大的博弈空间给机器博弈研究者带来了巨大挑战.目前围棋机器博弈多采用静态估值搜索与蒙特卡洛树搜索,故将时间差分算法引入至九路围棋机器博弈系统中,提出基于时间差分算法的围棋机器博弈系统模型,该博弈系统具有一定的自学习能力,能在不断的对弈中逐步提高博弈能力.通过与采用α-β搜索算法的博弈系统进行实际对弈,证明了该方法的可行性.     

围棋新天地

围棋新天地围棋起源于中国，是一项高智慧的运动项目，深受国人的喜爱。因此围棋爱好者众多。闲暇时或摆棋谱，或邀上棋友杀个天昏地暗。网络在中国的出现为围棋爱好者带来了更大的乐趣。至今让我记忆犹新的是有一次网上来了一位国内著名九段，顿时所有棋迷都向他致敬，屏...     

基于WWW的围棋棋谱演示软件的设计

介绍采用Java结合ASP编程实现的围棋棋谱演示软件,用Java技术实现用户界面设计及用户的动态交互,用ASP技术实现对棋谱数据库表格的访问。     

基于模糊聚类的神经网络求解围棋死活博弈

围棋死活求解非常消耗计算资源,博弈树的节点数随分支因子和深度的增加而呈指数级增长,使得传统的完全遍历博弈树的搜索不足以胜任.文中提出了一种基于模糊聚类的神经网络方法,利用模式识别和模糊属性检测,为涉及到外部劫争、循环规避、哈希置换、证明树等多方面问题的围棋博弈死活求解,构建了基于神经网络的棋型聚类分析器,快速又有效地极大减小了博弈树的分支因子,节约了死活求解所耗费的计算时间和内存空间.实验证明取得了比较理想的结果,研究表明把自学习能力赋予程序是提高计算机围棋博弈性能的有效途径.     

ASP动态网页中页面元素间的数据关联处理技术

介绍采用Ｊａｖａ结合ＡＳＰ编程实现的围棋棋谱演示软件,用Ｊａｖａ技术实现用户界面设计及用户的动态交互,用ＡＳＰ技术实现对棋谱数据库表格的访问。     

基于WinCE应用程序的围棋游戏开发

机器博弈,也称计算机博弈,即让计算机下棋。围棋是一种策略性二人棋类游戏,使用格状棋盘及黑白二色棋子进行对弈。文中计算机围棋游戏引擎的开发采用马尔科夫决策模型,使用人工智能的知识,含有大量计算,整个计算紧密依赖于系统资源,计算量越大,引擎的选点越精确,棋力越高。针对嵌入式系统软硬件的特定性,其资源和计算能力的局限性,本文主要完成了两个工作：一是将实验室适用于PC的游戏引擎移植到WinCE,开发适合嵌入式系统的围棋游戏引擎,实现大规模计算的移植,使游戏引擎在嵌入式有限的资源上,通过精简的计算量,达到不错的效果;二是实现WinCE上围棋游戏前台界面的开发。     

Life in the game of Go

The Oriental game of Go contains a unique method by which pieces, called stones, are captured and made safe from capture. A group of stones safe from capture is called safe, unconditionally alive, or similar terms. Life or its lack can be determined by lookahead through the game tree, at some expense. We present a graph-theoretic static analysis of the board arrangement which determines unconditional life or its lack, together with proofs of its equivalency to look ahead. An algorithm for the static evaluation is given and we argue that it is the preferable method for computer Go play. These results constitute the first realistic theorems in the theory of Go.     

Life in the game of Go

The Oriental game of Go contains a unique method by which pieces, called stones, are captured and made safe from capture. A group of stones safe from capture is called safe, unconditionally alive, or similar terms. Life or its lack can be determined by lookahead through the game tree, at some expense. We present a graph-theoretic static analysis of the board arrangement which determines unconditional life or its lack, together with proofs of its equivalency to look ahead. An algorithm for the static evaluation is given and we argue that it is the preferable method for computer Go play. These results constitute the first realistic theorems in the theory of Go.     

Evolving Neural Networks to Play Go

Go is a difficult game for computers to master, and the best go programs are still weaker than the average human player. Since the traditional game playing techniques have proven inadequate, new approaches to computer go need to be studied. This paper presents a new approach to learning to play go. The SANE (Symbiotic, Adaptive Neuro-Evolution) method was used to evolve networks capable of playing go on small boards with no pre-programmed go knowledge. On a 9 × 9 go board, networks that were able to defeat a simple computer opponent were evolved within a few hundred generations. Most significantly, the networks exhibited several aspects of general go playing, which suggests the approach could scale up well.     

强化学习及其在电脑围棋中的应用

强化学习是一类特殊的机器学习, 通过与所在环境的自主交互来学习决策策略, 使得策略收到的长期累积奖赏最大. 最近, 在围棋和电子游戏等领域, 强化学习被成功用于取得人类水平的操作能力, 受到了广泛关注. 本文将对强化学习进行简要介绍, 重点介绍基于函数近似的强化学习方法, 以及在围棋等领域中的应用.     

Soft-computing-based emotional expression mechanism for game of computer Go

The game of Go is considered one of the most complicated games in the world. One Go game is divided into three stages: the opening, the middle, and the ending stages. Millions of people regularly play Go in countries around the world. The game is played by two players. One is White and another is Black. The players alternate placing one of their stones on an empty intersection of a square grid-patterned game board. The player with more territory wins the game. This paper proposes a soft-computing-based emotional expression mechanism and applies it to the game of computer Go to make Go beginners enjoy watching Go game and keep their tension on the game. First, the knowledge base and rule base of the proposed mechanism are defined by following the standards of the fuzzy markup language. The soft-computing mechanism for Go regional alarm level is responsible for showing the inferred regional alarm level to Go beginners. Based on the inferred board situation, the fuzzy inference mechanisms for emotional pleasure and arousal are responsible for inferring the pleasure degree and arousal degree, respectively. An emotional expression mapping mechanism maps the inferred degree of pleasure and degree of arousal into the emotional expression of the eye robot. The protocol transmission mechanism finally sends the pre-defined protocol to the eye robot via universal serial bus interface to make the eye robot express its emotional motion. From the experimental results, it shows that the eye robot can support Go beginners to have fun and retain their tension while watching or playing a game of Go.     

In the News

This paper discusses how some artificial intelligence (AI) researchers and search experts are using AI methods to try to improve the accuracy of video search results. One example is a University of Oxford project in which researchers use statistical machine learning, specifically computer vision methods for face detection and facial feature localization, to provide automatic annotation of video with information about all the content of the video. Another example is the video search engine from Blinkx that objectively analyzes video content using speech recognition and matches the spoken words to context gleaned from a massive database. Finally, researchers at Dartmouth University are working on a technology that shows whether images or video clips have been doctored. This technique uses support vector machines to differentiate computer-generated images from photographic images. The paper goes on to discuss computer Go programs. Go is an ancient Asian board game which has become a challenge for AI researchers around the world. Go is resistant to Deep Blue's brute-force search of the game tree; the number of possible moves is too large. This inspires researchers to develop hybrid methods combining different methods and algorithms     

使用不同的博弈树搜索算法解决计算机围棋的吃子问题

使用Alpha Beta搜索和proof number (pn) 搜索解决计算机围棋的吃子问题.对吃子问题形式化并给出了简单有效的评估函数.Alpha Beta搜索使用了包括置换表在内的各种扩展技术.Pn搜索使用了包括df pn在内的4种变体.研究结果显示,对于解决吃子问题pn搜索优于Alpha Beta搜索.并且搜索过程中所产生的数据的一些模式可以帮助在结果未知的情况下对结果进行预测.所设计的算法可以用于解决单独的吃子问题或者计算机围棋比赛中的吃子计算.     

Learning to predict life and death from Go game records

This article presents a new learning system for predicting life and death in the game of Go. It is called Gone. The system uses a multi-layer perceptron classifier which is trained on learning examples extracted from game records. Blocks of stones are represented by a large amount of features which enable a rather precise prediction of life and death. On average, Gone correctly predicts life and death for 88% of all the blocks that are relevant for scoring. Towards the end of a game the performance increases up to 99%. A straightforward extension for full-board evaluation is discussed. Experiments indicate that the predictor is an important component for building a strong full-board evaluation function.