改进深度本地化水平分析：游戏分析如何提供帮助
Improving Deep Localized Level Analysis: How Game Logs Can Help
摘要：
球员建模是与理解球员相关的研究领域。在这个领域的一个追求是影响预测：预测一个游戏将给玩家带来什么感觉的能力。我们提出了新的改进，通过使用深度卷积神经网络（CNN）来预测在游戏事件日志上训练的玩家体验，并结合本地化的级别结构信息来影响预测。我们在基于《超级马里奥兄弟》（《无限马里奥兄弟》）的关卡上测试我们的方法。《超级马里奥兄弟》：失落的关卡（Gwario），以及原始的超级马里奥兄弟关卡。我们的表现优于之前的工作，并证明了对玩家日志进行训练的实用性，即使在跨域玩家建模的测试时缺乏它们。
英文摘要：
Player modelling is the field of study associated with understanding players. One pursuit in this field is affect prediction: the ability to predict how a game will make a player feel. We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience trained on game event logs in tandem with localized level structure information. We test our approach on levels based on Super Mario Bros. (Infinite Mario Bros.) and Super Mario Bros.: The Lost Levels (Gwario), as well as original Super Mario Bros. levels. We outperform prior work, and demonstrate the utility of training on player logs, even when lacking them at test time for cross-domain player modelling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
