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

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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.