UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI
Keywords:Personalized Game AI, Deep Neural Networks, User Playing Style
AbstractThis paper tries to resolve long waiting time to find a matching person in player versus player mode of online sports games, such as baseball, soccer and basketball. In player versus player mode, game playing AI which is instead of player needs to be not just smart as human but also show variety to improve user experience against AI. Therefore a need to design game playing AI agents with diverse personalized styles rises. To this end, we propose a personalized game AI which encodes user style vectors and card style vectors with a general DNN, named UCSM-DNN. Extensive experiments show that UCSM-DNN shows improved performance in terms of personalized styles, which enrich user experiences. UCSM-DNN has already been integrated into popular mobile baseball game: MaguMagu 2021 as personalized game AI.
How to Cite
Choe, D., Jo, Y., Kang, S., An, S., & Oh, I. (2022). UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13158-13160. https://doi.org/10.1609/aaai.v36i11.21713
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