Many works in the domain of artificial intelligence in games focus on board or video games due to the ease of reimplementing their mechanics. Decision-making problems in real-world sports share many similarities to such domains. Nevertheless, not many frameworks on sports games exist. In this paper, we present the tennis match simulation environment \textit{Match Point AI}, in which different agents can compete against real-world data-driven bot strategies. Next to presenting the framework, we highlight its capabilities by illustrating, how MCTS can be used in Match Point AI to optimize the shot direction selection problem in tennis. While the framework will be extended in the future, first experiments already reveal that generated shot-by-shot data of simulated tennis matches show realistic characteristics when compared to real-world data. At the same time, reasonable shot placement strategies emerge, which share similarities to the ones found in real-world tennis matches.

翻译：游戏人工智能领域的许多研究聚焦于棋盘或电子游戏，因其机制易于重新实现。现实世界体育中的决策问题与此类领域存在诸多相似之处。然而，针对体育游戏的现有框架并不多见。本文提出网球比赛模拟环境《Match Point AI》，其中不同智能体可与基于真实世界数据驱动的机器人策略进行对抗。在介绍该框架的同时，我们通过展示如何运用蒙特卡洛树搜索（MCTS）优化网球击球方向选择问题，来凸显其功能特性。虽然该框架未来将持续扩展，但初步实验已表明：相较于真实数据，模拟网球比赛生成的逐拍击球数据呈现出符合现实的特征。与此同时，模拟中涌现出合理的击球落点策略，这些策略与真实网球比赛中观察到的模式具有相似性。