Reading Between the Lines – Towards an Algorithm Exploiting In-game Behaviors to Learn Preferences in Gameful Systems
Reading Between the Lines – Towards an Algorithm Exploiting In-game Behaviors to Learn Preferences in Gameful Systems
复制标题
言外之意——一种利用游戏内行为来学习游戏系统偏好的算法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Marconi
中科院分区：
文献类型：
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作者：
Enrica Loria;A. Marconi
玩家的保持力可以通过调查玩家正在与之交互的游戏元素是否符合他们的喜好以及定制游戏动态以满足他们的偏好来培养。因此，自适应游戏是游戏用户研究领域和游戏行业广泛关注的话题。考虑到往往缺乏关于参与者偏好的明确信息，需要采取其他办法。当可用的游戏数据由于所采用的系统的简单性而受到限制时，该任务变得更具挑战性，因为它发生在与复杂的娱乐游戏或严肃游戏相反的游戏系统中。在这项工作中，我们提出了一个算法，利用用户行为作为一个隐式组件来计算玩家的喜好，通过测量他们的活动水平。应用领域是一个有说服力的游戏系统，可定制的游戏元素是单人挑战。该算法使用离线游戏数据来计算每个可行选项的偏好得分。然后将结果与根据玩家在游戏中的选择计算出的地面实况进行比较。我们的研究结果表明，玩家的行为可以用来通知定制的游戏元素的生成。
Players’ retainment can be fostered by investigating whether the game elements players are interacting with are to their liking and tailoring game dynamics to meet their preferences. Thus, adaptive gameplay is a widely interesting topic in both the Game User Research field and the game industry. Considering that explicit information on players’ preferences often lacks, alternative approaches are needed. This task becomes even more challenging when the gameplay data available is limited due to the simplicity of the system employed, as it occurs in gameful systems in contrast to complex entertainment games or serious games. In this work, we propose an algorithm that exploits user behaviors as an implicit component to compute players’ preferences by measuring their level of activity. The application domain is a persuasive gameful system, and the customizable game elements are single-player challenges. The proposed algorithm uses offline gameplay data to compute a preference score for every viable option. The outcomes are then compared against a ground truth calculated from players’ in-game choices. Our findings suggest that players’ behaviors can be used to inform the generation of tailored game elements.