英语翻译Artificial Intelligence in the Game of Go By Katie Hafne

英语翻译
Artificial Intelligence in the Game of Go
By Katie Hafner
Early in the film “A Beautiful Mind”,the mathematician John Nash is seen sitting in a Princeton courtyard,hunched over a playing board covered with small black and white pieces that look like pebbles.He was playing Go,an ancient Asian game.Frustration at losing that game inspired the real Nash to pursue the mathematics of game theory,research for which he eventually was awarded a Nobel Prize.
In recent years,computer experts,particularly those specializing in artificial intelligence,have felt the same fascination and frustration.Programming other board games has been a relative snap.Even chess has succumbed to the power of the processor.Five years ago,a chess-playing computer called Deep Blue not only beat but thoroughly humbled Garry Kasparov,the world champion at that time.That is because chess,while highly complex,can be reduced to a matter of brute force computation.Go is different.Deceptively easy to learn,either for a computer or a human,it is a game of such depth and complexity that it can take years for a person to become a strong player.To date,no computer has been able to achieve a skill level beyond that of the casual player.
The game is played on a board divided into a grid of 19 horizontal and 19 vertical lines.Black and white pieces called stones are placed one at a time on the grid’s intersections.The object is to acquire and defend territory by surrounding it with stones.Programmers working on Go see it as more accurate than chess in reflecting the ways the human mind works.The challenge of programming a computer to mimic that process goes to the core of artificial intelligence,which involves the study of learning and decision-making,strategic thinking,knowledge representation,pattern recognition and perhaps most intriguingly,intuition.
Danny Hillis,a computer designer and chairman of the technology company Applied Minds,said the depth of Go made it ripe for the kind of scientific progress that came from studying one example in great detail.“We want the equivalent of a fruit fly to study,” Hillis said.“Chess was the fruit fly for studying logic.Go may be the fruit fly for studying intuition.”
Along with intuition,pattern recognition is a large part of the game.
While computers are good at crunching numbers,people are naturally good at matching patterns.Humans can recognize an acquaintance at a glance,even from the back.

围棋中的人工智能
——卡蒂·火夫纳
早在电影《美丽的头脑》中,我们就看到数学家约翰·纳什坐在普林斯顿的庭院中,躬身在一块布满像鹅卵石一样的黑白旗子的木板上玩游戏.他是在下围棋,一个古老的亚洲游戏.失败的挫折感激励了纳什从事对博弈论的数学研究,最终他的研究成果获得了诺贝尔奖.
近年来,电脑专家尤其是那些擅长人工智能的电脑专家们也产生了同样的兴趣和挫折感.用程序控制棋盘游戏的想法突然产生.甚至连象棋也已经屈服于处理器的能力.5年前,一个叫做深蓝的电脑象棋选手不仅打败而且彻底地挫败了凯里·卡斯帕洛夫,那时的世界冠军.那是因为象棋非常复杂,可以被归纳为强力的计算.围棋与它不同.对于电脑和人类来说看似容易学习,但是它是一个十分深奥和复杂的游戏,要想成为很强大的高手需要花费很多年的时间.到目前为止,没有电脑能够超越临时玩家的能力.
这项游戏是在一个横竖各分成19个格子的棋盘上进行的.每人每次把一个黑色或白色的棋子放在格子的交叉点上.目的通过用棋子围住来取胜或防御领地.致力于围棋研究的程序师发现围棋需要人脑的反映远比象棋精确.设计一台电脑来模仿人脑的进程是人工智能面临的最核心的挑战,包括对学习、决策、战略、知识表现、模式识别和可能更有趣的直觉的研究.
丹尼·希尔斯,电脑设计师也是应用智力科技公司的主席,曾说围棋的深奥使得它在科学取得的详细研究案例的进步中相对成熟.“我们需要进行同等的研究”希尔斯说到,“象棋是研究逻辑的.围棋可能是研究直觉的.”
同直觉一样,模式识别也在游戏中占有重要地位.电脑擅长琐碎的数字,人类自然擅长匹配模式.人类瞟一眼都能认出熟人,甚至可以从背后认出.