Tuning evaluation functions by maximizing concordance
Tuning evaluation functions by maximizing concordance
复制标题
通过最大化一致性来调整评估函数
DOI：
10.1016/j.tcs.2005.09.047
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Tony Marsland
中科院分区：
文献类型：
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作者：
Dave Gomboc;M. Buro;Tony Marsland
启发式搜索的有效性直接取决于搜索空间中状态的启发式评估的质量。鉴于过去半个世纪对计算机国际象棋的大量研究工作，对于确定对评估函数的拟议更改是否有益的问题没有给予足够的重视。我们认为，评估函数从国际象棋位置到启发值的映射是序数尺度的，而不是区间尺度的。我们确定了一个适合评估评估函数质量的稳健指标，并提出了一种有效计算该指标的新方法。最后，我们在此指标上应用经验梯度上升程序（也是我们设计的）来优化计算机国际象棋程序的评估函数的特征权重。我们的实验表明，以这种方式调整的评估函数权重与手动调整的权重具有相同的性能。
Heuristic search effectiveness depends directly upon the quality of heuristic evaluations of states in a search space. Given the large amount of research effort devoted to computer chess throughout the past half-century, insufficient attention has been paid to the issue of determining if a proposed change to an evaluation function is beneficial. We argue that the mapping of an evaluation function from chess positions to heuristic values is of ordinal, but not interval scale. We identify a robust metric suitable for assessing the quality of an evaluation function, and present a novel method for computing this metric efficiently. Finally, we apply an empirical gradient-ascent procedure, also of our design, over this metric to optimize feature weights for the evaluation function of a computer-chess program. Our experiments demonstrate that evaluation function weights tuned in this manner give equivalent performance to hand-tuned weights.