Monte Carlo Search gives excellent results in multiple difficult combinatorial problems. Using a prior to perform non uniform playouts during the search improves a lot the results compared to uniform playouts. Handmade heuristics tailored to the combinatorial problem are often used as priors. We propose a method to automatically compute a prior. It uses statistics on solved problems. It is a simple and general method that incurs no computational cost at playout time and that brings large performance gains. The method is applied to three difficult combinatorial problems: Latin Square Completion, Kakuro, and Inverse RNA Folding.

翻译：蒙特卡洛搜索在多种困难组合问题中展现出优异性能。在搜索过程中采用非均匀模拟（非均匀博弈）时，使用先验知识可显著提升效果，优于均匀模拟方案。针对特定组合问题设计的人工启发式策略常被用作先验知识。本文提出一种自动计算先验的方法，该方法基于已解问题的统计信息。该方案简单通用，在模拟阶段不产生额外计算成本，同时能带来显著的性能提升。本方法被应用于三种困难组合问题：拉丁方填充、数谜游戏与逆RNA折叠。