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.

Abstract A device‐free human counting (DFC) algorithm that uses fine‐grained subcarrier information from WiFi devices, called channel state information (CSI), to count the number of people in indoor environments is proposed. The DFC algorithm extracts the features of average attenuation and variation of CSI amplitudes caused by human motions, and puts the features into a training process to improve the counting accuracy. Through a bootstrapping process, the DFC can estimate the number of people standing in the middle of a WiFi link by constructing a probability model with the CSI signals at a receiver side. With this human counting capability, the DFC can support the efficient monitoring and automatic control of electrical devices (e.g. air conditioner, heater, bulb, and beam projector) indoors. Through a real implementation and experiments, it is shown that the DFC algorithm outperforms the state‐of‐the‐art DFC algorithm based on RSSI in indoor environments with human mobility. For a dynamic‐target case in a meeting room, for example, DFC can predict the number of people in an indoor space with an accuracy about 98% at best.