Statistical Analysis of Player Behavior in Minecraft
Statistical Analysis of Player Behavior in Minecraft
复制标题
我的世界玩家行为统计分析
DOI：
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复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Gross
中科院分区：
文献类型：
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作者：
Stephan Müller;Mubbasir Kapadia;Seth Frey;Severin Klingler;R. Mann;B. Solenthaler;R. Sumner;M. Gross
交互式虚拟世界在各种各样的人工现实中提供新的个人和社交体验。它们在研究人们如何互动以及社会如何运作和演变方面也具有巨大的潜力。游戏中行为数据的系统收集和分析对于增强玩家体验、促进有效管理和释放在线社会的科学潜力具有不可估量的价值。本文详细介绍了在 Minecraft 中收集玩家数据的框架的开发。我们提供了一个完整的解决方案，可以部署在 Minecraft 服务器上，将收集到的数据发送到集中服务器，以便研究人员、玩家和服务器管理员进行可视化和分析。使用该框架，我们收集并分析了超过 14 人日的活跃游戏玩法。我们构建了一个分类工具，通过观察玩家每时每刻的游戏行为来识别高级玩家的行为。玩家和服务器管理员可以使用突出空间行为的热图可视化来评估游戏体验。我们的数据收集和分析框架提供了通过对虚拟世界的大规模观察研究来了解个人行为、环境因素和社会系统如何相互作用的机会。
Interactive Virtual Worlds offer new individual and social experiences in a huge variety of artificial realities. They also have enormous potential for the study of how people interact, and how societies function and evolve. Systematic collection and analysis of in-play behavioral data will be invaluable for enhancing player experiences, facilitating effective administration, and unlocking the scientific potential of online societies. This paper details the development of a framework to collect player data in Minecraft. We present a complete solution which can be deployed on Minecraft servers to send collected data to a centralized server for visualization and analysis by researchers, players, and server administrators. Using the framework, we collected and analyzed over 14 person-days of active gameplay. We built a classification tool to identify high-level player behaviors from observations of their moment-by-moment game actions. Heat map visualizations highlighting spatial behavior can be used by players and server administrators to evaluate game experiences. Our data collection and analysis framework offers the opportunity to understand how individual behavior, environmental factors, and social systems interact through large-scale observational studies of virtual worlds.
预测 EVE Online 中玩家的意外涌入
DOI：
10.1109/cig.2014.6932878
发表时间：
2014
期刊：
2014 IEEE Conference on Computational Intelligence and Games
影响因子：
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作者：
Roman Garnett;Thomas Gärtner;Timothy Ellersiek;Eyjolfur Gudmondsson;Petur Oskarsson
通讯作者：
Petur Oskarsson