思维方式与团队竞赛游戏表现与享受

Hao Wang, Hao-Tsung Yang, Chuen-Tsai Sun
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引用次数: 16

摘要

目前几乎所有基于玩家技能等级的团队竞赛游戏匹配系统都包含了一些算法,这些算法旨在创建由技能水平相似的玩家组成的团队。然而,这些系统忽略了游戏风格这一重要因素。本文将运用Sternberg的思维风格理论和个人历史数据对《英雄联盟》(LoL)玩家进行分类,分析游戏风格对团队竞技游戏乐趣的影响。大约64000场比赛的数据来自LoLBase网站,涉及18.5万名球员。当游戏持续26分钟或更短(最早的投降时间)时,游戏乐趣就会降低。统计分析结果表明,具有特定比赛风格的球员更有可能提高比赛乐趣和团队实力。我们还使用神经网络模型来测试比赛风格信息在预测比赛质量方面的有用性。我们希望这些结果将有助于建立更有效的婚介系统。
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Thinking Style and Team Competition Game Performance and Enjoyment
Almost all current matchmaking systems for team competition games based on player skill ratings contain algorithms designed to create teams consisting of players at similar skill levels. However, these systems overlook the important factor of playing style. In this paper, we analyze how playing style affects enjoyment in team competition games, using a mix of Sternberg's thinking style theory and individual histories in the form of statistics from previous matches to categorize League of Legend (LoL) players. Data for approximately 64 000 matches involving 185 000 players were taken from the LoLBase website. Match enjoyment was considered low when games lasted for 26 min or less (the earliest possible surrender time). Results from statistical analyses indicate that players with certain playing styles were more likely to enhance both game enjoyment and team strength. We also used a neural network model to test the usefulness of playing style information in predicting match quality. It is our hope that these results will support the establishment of more efficient matchmaking systems.
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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