用游戏视频无标签建模玩家体验，效果接近真实测量。
Label-Free Subjective Player Experience Modelling via Let's Play Videos
- 仅用游戏视频预测玩家情绪，无需人工标注。
- 在《愤怒的小鸟》中预测情感，与自评和生理数据高度相关。
- 适合想低成本构建玩家体验模型的研究者或开发者。
玩家体验建模（PEM）是将AI技术应用于分析玩家在游戏中的感受。传统PEM开发耗时费力，需专家手工标注或专门数据采集。本文提出一种新方法：从游戏实况视频中推断玩家体验。通过人类受试者实验，在《愤怒的小鸟》游戏中验证该方法可有效预测情感状态，其结果与自我报告及生理传感器测得的情感数据显著相关，证明了该方法的可行性与潜力。
原文摘要 · Abstract (English)
Player Experience Modelling (PEM) is the study of AI techniques applied to modelling a player's experience within a video game. PEM development can be labour-intensive, requiring expert hand-authoring or specialized data collection. In this work, we propose a novel PEM development approach, approximating player experience from gameplay video. We evaluate this approach predicting affect in the game Angry Birds via a human subject study. We validate that our PEM can strongly correlate with self-reported and sensor measures of affect, demonstrating the potential of this approach.
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