A recent trend within computational intelligence and games research is to investigate how to affect video game players’ in-game experience by designing and/or modifying aspects of game content. Analysing the relationship between game content, player behaviour and self-reported affective states constitutes an important step towards understanding game experience and constructing effective game adaptation mechanisms. This papers reports on further refinement of a method to understand this relationship by analysing data collected from players, building models that predict player experience and analysing what features of game and player data predict player affect best. We analyse data from players playing 780 pairs of short game sessions of the platform game Super Mario Bros, investigate the impact of the session size and what part of the level that has the major affect on player experience. Several types of features are explored, including item frequencies and patterns extracted through frequent sequence mining.
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机译：计算智能和游戏研究的最新趋势是研究如何通过设计和/或修改游戏内容来影响视频游戏玩家的游戏体验。分析游戏内容，玩家行为和自我报告的情感状态之间的关系，是理解游戏体验和构建有效的游戏适应机制的重要一步。本文报告了通过分析从玩家收集的数据，建立预测玩家体验的模型并分析游戏的哪些特征以及玩家数据预测玩家影响最大的方法来进一步了解这种关系的方法。我们分析了来自平台游戏超级马里奥兄弟（Super Mario Bros）的780对短游戏会话的玩家的数据，调查了会话大小的影响以及影响玩家体验的主要因素。探索了几种类型的特征，包括通过频繁序列挖掘提取的项目频率和样式。
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