论文标题
以有限的外观下棋
Playing Chess with Limited Look Ahead
论文作者
论文摘要
多年来，我们已经看到许多机器学习方法解决了国际象棋游戏。但是，这些作品中的一个共同元素是需要精细优化的外观算法。这项研究的特别兴趣在于创造出高度能力但在其前景深度上受到限制的国际象棋引擎。我们训练一个深层神经网络，以充当静态评估功能，并伴随着相对简单的外观算法。我们表明，我们的静态评估功能已经编码了前面知识的某些外观，并且与经典评估功能相媲美。我们的国际象棋发动机的强度是通过将其提议的移动与Stockfish提出的动作进行比较来评估的。我们表明，尽管严格限制了向前的深度，但我们的引擎建议以同等强度的举动，约为$ 83 \％的样本位置。
We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of this research lies with creating a chess engine that is highly capable, but restricted in its look ahead depth. We train a deep neural network to serve as a static evaluation function, which is accompanied by a relatively simple look ahead algorithm. We show that our static evaluation function has encoded some semblance of look ahead knowledge, and is comparable to classical evaluation functions. The strength of our chess engine is assessed by comparing its proposed moves against those proposed by Stockfish. We show that, despite strict restrictions on look ahead depth, our engine recommends moves of equal strength in roughly $83\%$ of our sample positions.
