首页> 外文会议>IEEE International Conference on Cognitive Infocommunications >'Learn to play, noob!': The identification of ability profiles for different roles in an online multiplayer video game in order to improve the overal quality of the new player experience
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'Learn to play, noob!': The identification of ability profiles for different roles in an online multiplayer video game in order to improve the overal quality of the new player experience

机译:“学会玩,noob!”:在在线多人游戏中的不同角色的能力概况的识别,以提高新的玩家体验的高度质量

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Being a new player in a multiplayer game is not an easy experience. If the game requires coordinated team play for success, an inexperienced player can receive a high amount of abuse because of what others view as bad plays. While the companies try to alleviate this problem by focusing on the offenders, we present another approach that can also help in decreasing toxic behavior. In our paper we introduce the first steps at researching what it requires to perform well in any given role in a popular multi player game, League of Legends. Using focus groups and online questionnaires we narrow down the list of abilities to 6, in which different profiles would predict different most successful playstyles. Based on these ability profiles the game itself should give more customized tutorial sessions and recommendations to new players who in turn will have an overall improved first impression. Through having better performance, we believe, that some of the toxic behavior experienced by new players can be extinguished.
机译:作为多人游戏中的新玩家不是一种简单的体验。如果游戏需要协调的团队成功,那么一个缺乏经验的球员可以获得大量的滥用,因为别人视为糟糕的戏剧。虽然公司尝试通过专注于违规者来缓解这一问题,但我们提出了另一种方法,也可以帮助降低毒性行为。在我们的论文中,我们介绍了研究在一个受欢迎的多人游戏,传说中的任何特定角色中表现出色的第一步。使用焦点小组和在线问卷我们将能力列表缩小到6,其中不同的简档将预测不同成功的Playstyles。基于这些能力的简档,游戏本身应该向新的玩家提供更多定制的教程会话和建议,他们又会具有整体改进的第一印象。通过具有更好的表现,我们相信,新球员经历的一些有毒行为可以熄灭。

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