论文标题
DOTA 2中的自动播放器标识
Automatic Player Identification in Dota 2
论文作者
论文摘要
Dota 2是一种流行的多人在线视频游戏。像许多在线游戏一样，玩家大多是匿名的，仅与可以在多个人之间容易获得，出售和共享的在线帐户绑定。这使得很难跟踪或禁止在线表现出不良行为的玩家。在本文中，我们提出了一种机器学习方法，以识别玩家如何玩游戏的“数字指纹”，而不是通过帐户。我们使用有关鼠标运动，游戏内统计和从比赛重播提取的游戏策略的数据，并表明为了获得最佳结果，所有这些都是必要的。对于预测同一玩家是否播放了两种不同匹配的问题，我们能够获得95 \％预测的准确性。
Dota 2 is a popular, multiplayer online video game. Like many online games, players are mostly anonymous, being tied only to online accounts which can be readily obtained, sold and shared between multiple people. This makes it difficult to track or ban players who exhibit unwanted behavior online. In this paper, we present a machine learning approach to identify players based a `digital fingerprint' of how they play the game, rather than by account. We use data on mouse movements, in-game statistics and game strategy extracted from match replays and show that for best results, all of these are necessary. We are able to obtain an accuracy of prediction of 95\% for the problem of predicting if two different matches were played by the same player.
