Game Balancing in Dominion: An Approach to Identifying Problematic Game Elements
Keywords:Bayesian Belief Network, Learning, Dominion
AbstractIn the popular card game Dominion, the configuration of game elements greatly affects the experience for players. If one were redesigning Dominion, therefore, it may be useful to identify game elements that reduce the number of viable strategies in any given game configuration - i.e. elements that are unbalanced. In this paper, we propose an approach that assigns credit to the outcome of an episode to individual elements. Our approach uses statistical analysis to learn the interactions and dependencies between game elements. This learned knowledge is used to recommend elements to game designers for further consideration. Designers may then choose to modify the recommended elements with the goal of increasing the number of viable strategies.
How to Cite
Ford, C., & Ohata, M. (2022). Game Balancing in Dominion: An Approach to Identifying Problematic Game Elements. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12744-12750. https://doi.org/10.1609/aaai.v36i11.21552
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