Modelling Player Preferences in AR Mobile Games
Modelling Player Preferences in AR Mobile Games
复制标题
AR 手机游戏中玩家偏好建模
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
L. Tokarchuk
中科院分区：
文献类型：
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作者：
Vivek R. Warriar;John R. Woodward;L. Tokarchuk
在本文中，我们使用偏好学习技术来模拟 AR 手机游戏中玩家的情感偏好。这项探索性研究使用玩家行为来做出这些偏好预测。所描述的技术成功地以高精度预测了玩家的挫败感和挑战水平，而测试的所有其他偏好（无聊、兴奋和乐趣）的表现都比随机机会更好。本文介绍了我们开发的 AR 寻宝游戏、为收集玩家偏好数据而进行的用户研究、执行的分析以及用于对这些数据进行建模的偏好学习技术。这项工作的动机是通过使用这些计算模型来优化这些环境中的内容创建和游戏平衡系统，从而个性化玩家的体验。讨论了我们的技术的一般性、局限性和作为 AR 手机游戏个性化工具的可用性。
In this paper, we use preference learning techniques to model players’ emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players’ frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players’ experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed.