Transfer learning for cross-game prediction of player experience
Transfer learning for cross-game prediction of player experience
复制标题
用于玩家体验跨游戏预测的迁移学习
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Mohamed Abou
中科院分区：
文献类型：
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作者：
Noor Shaker;Mohamed Abou
几项关于跨域用户行为的研究揭示了一般的个性轨迹和行为模式。本文提出了一种定量的方法，利用一个游戏中玩家行为的知识来为另一个游戏中建立玩家体验模型的过程播种。我们研究了两种设置：在有监督的特征映射方法中，我们使用关于两个游戏中玩家行为的标签数据集。目标是建立特征之间的映射，以便可以通过简单的特征替换在一个数据集上使用构建在另一个数据集上的模型。对于无监督迁移学习场景，我们的目标是在未标记数据的基础上找到相关特征的共享空间。然后，共享空间中的特征被用来构建一个游戏的模型，该模型直接作用于另一个游戏的转移特征。我们实现并分析了这两种方法，我们表明，在域之间转移玩家体验的知识确实是可能的，并且在研究玩家的行为和设计用户研究时最终是有用的。
Several studies on cross-domain users' behaviour revealed generic personality trails and behavioural patterns. This paper, proposes quantitative approaches to use the knowledge of player behaviour in one game to seed the process of building player experience models in another. We investigate two settings: in the supervised feature mapping method, we use labeled datasets about players' behaviour in two games. The goal is to establish a mapping between the features so that the models build on one dataset could be used on the other by simple feature replacement. For the unsupervised transfer learning scenario, our goal is to find a shared space of correlated features based on unlabelled data. The features in the shared space are then used to construct models for one game that directly work on the transferred features of the other game. We implemented and analysed the two approaches and we show that transferring the knowledge of player experience between domains is indeed possible and ultimately useful when studying players' behaviour and when designing user studies.