论文标题
改善深度局部水平分析：游戏日志如何帮助
Improving Deep Localized Level Analysis: How Game Logs Can Help
论文作者
论文摘要
玩家建模是与理解玩家相关的研究领域。该领域的一种追求是影响预测：预测游戏将如何使玩家感觉到的能力。我们通过使用深层卷积神经网络（CNN）来预测在游戏事件日志中培训的玩家体验与局部级别结构信息，以预测玩家经验，以影响预测。我们根据超级马里奥兄弟（Infinite Mario Bros.）和超级马里奥兄弟（Super Mario Bros。）的水平测试方法：丢失的水平（Gwario）以及原始的Super Mario Bros.级别。我们的表现要超过事先工作，即使在测试时间缺少跨域玩家建模时，也证明了对玩家日志的培训的实用性。
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.
