国际象棋统计方法？ [英] Statistical approach to chess?
问题描述
阅读有关谷歌如何解决让我思考翻译问题。是否可以通过分析数百万的游戏，并确定主要是基于（完全？）统计最好的移动建立一个强大的国际象棋引擎？有几个这样的国际象棋数据库（这是一个拥有450万的游戏），以及一个可能权衡使用因素相同（或镜像或反射）的位置移动，如玩家所涉及的收视率，怎么老游戏（在提高国际象棋理论因子）等任何原因，这的不的是一个可行的方法来构建一个国际象棋引擎？
这样的事情已经完成：这是开放的基本概念图书。
由于比赛的性质，计算机人工智能是出了名的坏开始的时候，有这么多的可能性和最终目标仍遥遥领先。它开始以改善向中间，当战术的可能性开始形成，并且可以在比赛结束完美播放远远超过大多数人的能力。
要帮助AI做出好棋在开始的时候，许多发动机依赖于开放的书籍，而不是：中移动一个统计学方法得出的流程图，基本上是这样。高评级的玩家之间的多场比赛进行了分析，并建议硬coded到书，虽然位置仍然在书中，AI甚至不想，并简单地遵循什么书说。
有些人还可以记住打开的书（这主要是为什么菲舍尔发明了随机国际象棋的变异，因此该记忆开口就远不如有效）。部分原因是由于这一点，有时是一种非常规的举动是在开始做，不是因为它是统计上最好的移动根据病史，但precisely相反：它不是一个已知的位置，可以把你的对手（人或计算机）出书。
在光谱的另一端，有一种叫做最后阶段tablebase 的，这基本上是$ p的数据库$ pviously分析残局位置。由于仓位分别为previously搜索详尽，人们可以用它来实现完美播放：给定任意位置，可以立即决定它是否赢球，输球，或绘制，这有什么，以达到最佳的方法/避免的结局<。 / P>
在棋，这样的事情是只为开幕式和残局可行的，虽然。在中盘的复杂性是什么使游戏的趣味性。如果一个人能够通过查找表只是下棋的话，游戏就不会因为它是令人兴奋的，有趣的，而深。
Reading about how Google solves the translation problem got me thinking. Would it be possible to build a strong chess engine by analysing several million games and determining the best possible move based largely (completely?) on statistics? There are several such chess databases (this is one that has 4.5 million games), and one could potentially weight moves in identical (or mirrored or reflected) positions using factors such as the ratings of the players involved, how old the game is (to factor in improvements in chess theory) etc. Any reasons why this wouldn't be a feasible approach to building a chess engine?
Something like this is already done: it's the underlying concept of opening books.
Due to the nature of the game, computer AIs are notoriously bad in the beginning, when there are so many possibilities and the end goal is still far ahead. It starts to improve toward the middle, when tactical possibilities start to form, and can play perfectly in the end game far exceeding the capability of most humans.
To help the AI make good moves in the beginning, many engines rely on opening books instead: a statistically derived flowchart of moves, basically. Many games between highly rated players were analyzed, and recommendations are hard-coded into "the book", and while the positions are still in "the book", the AI doesn't even "think", and simply follow what "the book" says.
Some people can also memorize opening books (this is mostly why Fischer invented his random chess variant, so that memorization of openings becomes far less effective). Partly due to this, sometimes an unconventional move is made in the beginning, not because it's statistically the best move according to history, but precisely the opposite: it's not a "known" position, and can take your opponent (human or computer) "out of the book".
On the opposite end of the spectrum, there is something called endgame tablebase, which is basically a database of previously analyzed endgame positions. Since the positions were previously searched exhaustively, one can use this to enable perfect play: given any position, one can immediately decide if it's winning, losing, or draw, and what's the best way to achieve/avoid the outcome.
In chess, something like this is only feasible for the opening and endgame, though. The complexity of the middle game is what makes the game interesting. If one can play chess simply by looking up a table, then the game wouldn't be as exciting, interesting, and deep as it is.
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