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

随着桌面游戏的发展，它们变得比传统的同行(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，以及不确定性等几个例子。正是这种复杂性让它们在当前的世界中蓬勃发展，但这也使得创造这样的游戏变得更加困难。本研究使用人工智能玩家来研究不同的游戏策略，这些策略可以通过一系列衡量标准进行评估。我们的目标是研究如何使用现有的衡量标准来制定战略，并创建新的衡量标准来辅助游戏设计。拟议的贡献有两个方面：第一，一个帮助游戏设计师分析他们的游戏策略的工具；第二，为游戏开发新的指标，专注于评估更高级别的概念，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.