Discriminative Neural Network for Hero Selection in Professional Heroes of the Storm and DOTA 2
Discriminative Neural Network for Hero Selection in Professional Heroes of the Storm and DOTA 2
复制标题
风暴英雄和 DOTA 2 职业英雄选择的判别神经网络
DOI：
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发表时间：
2020
影响因子：
2.3
通讯作者：
L. Archambault
中科院分区：
文献类型：
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作者：
Daniel Gourdeau;L. Archambault
多人在线竞技场（MOBA）游戏是最流行的在线游戏类型之一。每年一度的锦标赛吸引了大量的在线观众，并为获胜团队提供了丰厚的奖金。游戏开始前的角色选择（选秀）在游戏的进行方式中起着重要作用，并且可以给任何一方带来很大的优势。因此，职业球队试图通过选择最佳的球队阵容来对抗对手，从而最大化他们的获胜机会。然而，制图是一个复杂的过程，需要深厚的游戏知识和准备，这使得它充满压力且容易出错。在本文中，我们提出了一种基于判别神经网络建议的自动绘图系统，并评估了它在 MOBA 风暴英雄 (HotS) 和 DOTA 2 上的表现。我们提出了一种适当利用非常异构的数据集的方法，该数据集聚合了来自不同游戏版本的数据。对专业游戏的起草者测试表明，实际选择的英雄出现在我们的起草工具确定的前三名中，HotS 的概率为 30.4%，DOTA 2 的概率为 17.6%。通过这种方法获得的性能超过了之前报告的所有结果。
Multiplayer online battle arena (MOBAs) games are one of the most popular types of online games. Annual tournaments draw large online viewership and reward the winning teams with large monetary prizes. Character selection prior to the start of the game (draft) plays a major role in the way the game is played and can give a large advantage to either team. Hence, professional teams try to maximize their winning chances by selecting the optimal team composition to counter their opponents. However, drafting is a complex process that requires deep game knowledge and preparation, which makes it stressful and error-prone. In this article, we present an automatic drafter system based on the suggestions of a discriminative neural network and evaluate how it performs on the MOBAs Heroes of the Storm (HotS) and DOTA 2. We propose a method to appropriately exploit very heterogeneous data sets that aggregates data from various versions of the games. Drafter testing on professional games shows that the actual selected hero was present in the top three determined by our drafting tool 30.4% of the time for HotS and 17.6% for DOTA 2. The performance obtained by this method exceeds all previously reported results.