Versatile Uncertainty Quantification of Contrastive Behaviors for Modeling Networked Anagram Games

Versatile Uncertainty Quantification of Contrastive Behaviors for Modeling Networked Anagram Games
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用于网络化 Anagram 游戏建模的对比行为的多功能不确定性量化

DOI:
10.1007/978-3-030-93409-5_53
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发表时间:
2021
期刊:
Complex Networks and their Applications
影响因子:
--
通讯作者:
Kuhlman, Chris J
Kuhlman, Chris J
中科院分区:
--
文献类型:
--
作者:
Hu, Zhihao;Deng, Xinwei;Kuhlman, Chris J

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在网络字谜游戏中,每个团队成员都有一组字母,成员们一起拼出尽可能多的单词。他们可以通过交流网络分享字母,帮助邻居组成单词。玩家的行为具有可变性,例如,玩家之间的信件请求数量、对信件请求的回复数量以及形成的单词数量可能存在很大差异。因此,理解玩家行为的不确定性和可变性非常重要。在这项工作中,我们提出了行为的通用不确定性量化(VUQ)来建模网络变位游戏。具体来说,所提出的方法侧重于构建玩家行为的对比模型,根据最差、平均和最佳表现量化玩家行为。此外,我们构建基于代理的模型,并使用这些VUQ方法执行基于代理的仿真,以评估模型构建方法并了解不确定性的影响。我们相信这种方法也适用于其他网络游戏。
In a networked anagram game, each team member is given a set of letters and members collectively form as many words as possible. They can share letters through a communication network in assisting their neighbors in forming words. There is variability in behaviors of players, e.g., there can be large differences in numbers of letter requests, of replies to letter requests, and of words formed among players. Therefore, it is of great importance to understand uncertainty and variability in player behaviors. In this work, we propose versatile uncertainty quantification (VUQ) of behaviors for modeling the networked anagram game. Specifically, the proposed methods focus on building contrastive models of game player behaviors that quantify player actions in terms of worst, average, and best performance. Moreover, we construct agent-based models and perform agent-based simulations using these VUQ methods to evaluate the model building methodology and understand the impact of uncertainty. We believe that this approach is applicable to other networked games.
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