The use of AI strategy data in redefining modern tabletop game design

随着桌面游戏的发展，它们已经变得比传统的游戏（国际象棋，围棋）复杂得多，它们现在有隐藏的信息，更多种类的组件和不确定性。这种复杂性使它们在当今世界蓬勃发展，但这也使创建此类游戏变得更加困难。本研究使用AI玩家来研究不同的游戏策略，这些策略可以通过一系列指标进行评估。我们的目标是研究如何使用现有的战略指标，并创建新的，以协助游戏设计。这项研究的贡献有两个方面：第一，它是一个帮助游戏设计师分析游戏策略的工具;第二，它为游戏开发了新的衡量标准，专注于评估更高层次的概念，如玩家参与度和可销售性。这将允许游戏设计师创建更好的游戏，并帮助玩家获得更好的游戏体验。

With the evolution of tabletop games, they have become much more complex than their traditional counterparts (chess, go), they now have hidden information, a higher variety of components, and uncertainty as a few examples. This complexity is what made them thrive in the current world, but this also makes the creation of such games more difficult. This research use AI players to investigate different game strategies which can be evaluated through a series of metrics. We aim to investigate how to use existing metrics for strategy and create new ones to assist game design. The proposed contribution is two-fold: first, a tool that assists game designers in analyzing their game strategies; second, the development of new metrics for games focused on evaluating higher-level concepts such as player engagement and sellability. This will allow game designers to create better games and also assist players in achieving a better play experience.