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   Gray, Robert C; Zhu, Jichen; Ontañón, Santiago (January 2021, Proceedings of the 3rd IEEE Conference on Games)

   null (Ed.)

   This paper focuses on player modeling in multiplayer adaptive games. While player modeling has received a significant amount of attention, less is known about how to use player modeling in multiplayer games, especially when an experience management AI must make decisions on how to adapt the experience for the group as a whole. Specifically, we present a multi-armed bandit (MAB) approach for modeling groups of multiple players. Our main contributions are a new MAB frame- work for multiplayer modeling and techniques for addressing the new challenges introduced by the multiplayer context, extending previous work on MAB-based player modeling to account for new group-generated phenomena not present in single-user models. We evaluate our approach via simulation of virtual players in the context of multiplayer adaptive exergames. 

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