Many enhancements to Monte-Carlo Tree Search (MCTS) have been proposed over almost two decades of general game playing and other artificial intelligence research. However, our ability to characterise and understand which variants work well or poorly in which games is still lacking. This paper describes work on an initial dataset that we have built to make progress towards such an understanding: 268,386 plays among 61 different agents across 1494 distinct games. We describe a preliminary analysis and work on training predictive models on this dataset, as well as lessons learned and future plans for a new and improved version of the dataset.

翻译：在近二十年的通用游戏博弈及其他人工智能研究中，已提出了许多针对蒙特卡洛树搜索（MCTS）的改进方法。然而，我们仍缺乏刻画和理解哪些变体在何种游戏中表现优异或低效的能力。本文介绍了为推进此类理解而构建的初始数据集相关工作：该数据集包含61种不同智能体在1494个不同游戏中的268,386次对局记录。我们描述了对此数据集的初步分析及预测模型训练工作，同时总结了经验教训，并提出了数据集新版改进的未来规划。