Behavioral Player Rating in Competitive Online Shooter Games

用于2D射击游戏的强化学习智能体

Reinforcement Learning Agent for a 2D Shooter Game

本文针对2D射击游戏中强化学习训练不稳定和样本效率低的问题，提出先行为克隆后强化学习的混合方法，并采用多头注意力网络，最终实现超过70%的稳定胜率。

Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry

Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for application in clinical and research settings. Here, we use computer vision to derive behavioral codes or concepts of a gold standard behavioral rating system, offering familiar interpretation for mental health professionals. Features were extracted …

Player Modeling using Behavioral Signals in Competitive Online Games

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. O…

知识感知的多意图对比学习用于多行为推荐

Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation

提出知识感知多意图对比学习模型，利用知识图谱构建意图并采用双对比学习缓解数据稀疏，提升多行为推荐的准确性。

From Pixels to Affect: A Study on Games and Player Experience

Is it possible to predict the affect of a user just by observing her behavioral interaction through a video? How can we, for instance, predict a user's arousal in games by merely looking at the screen during play? In this paper we address these questions by employing three dissimilar deep convolutional neural network architectures in our attempt to learn the underlying mapping between video streams of gameplay and the player's arousal. We test the algorithms in an annotated dataset of 50 gameplay videos of a survival shooter game and evaluate the deep learned models' capacity to classify high …

基于深度学习和机器学习的第一人称射击游戏服务器端反作弊瞄准外挂检测

Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning

研究第一人称射击游戏中瞄准外挂检测问题，通过识别包括时间序列数据和行为数据的特定特征，构建服务器端检测分类器YAACS，实验表明该方法可降低误报率。