UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI
Abstract
This 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.
Cite
Text
Choe et al. "UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21713Markdown
[Choe et al. "UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/choe2022aaai-ucsm/) doi:10.1609/AAAI.V36I11.21713BibTeX
@inproceedings{choe2022aaai-ucsm,
title = {{UCSM-DNN: User and Card Style Modeling with Deep Neural Networks for Personalized Game AI}},
author = {Choe, Daegeun and Jo, Youngbak and Kang, Shindong and An, Shounan and Oh, Insoo},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13158-13160},
doi = {10.1609/AAAI.V36I11.21713},
url = {https://mlanthology.org/aaai/2022/choe2022aaai-ucsm/}
}