论文标题
了解看门狗：发现如何发现游戏机器人
Understand Watchdogs: Discover How Game Bot Get Discovered
论文作者
论文摘要
长期以来，利用游戏机器人的恶意活动困扰了游戏行业。该游戏机器人打扰了其他游戏玩家并破坏了游戏的环境系统。由于这些原因，游戏行业尽力使用基于学习的检测来检测玩家角色之间的游戏机器人。但是，检测方法的一个问题是，它们没有提供有关其决策的合理解释。为了解决此问题，在这项工作中，我们研究了游戏机器人检测的解释能力。我们使用韩国MMORPG AION的数据集开发XAI模型，其中包括人类玩家和游戏机器人的游戏日志。已将多个分类模型应用于数据集，以通过应用可解释的模型来分析。这为我们提供了有关游戏机器人行为的解释，并评估了解释的真实性。此外，解释性有助于最大程度地减少虚假检测，这对人类参与者施加了不公平的限制。
The game industry has long been troubled by malicious activities utilizing game bots. The game bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry put their best efforts to detect the game bots among players' characters using the learning-based detections. However, one problem with the detection methodologies is that they do not provide rational explanations about their decisions. To resolve this problem, in this work, we investigate the explainabilities of the game bot detection. We develop the XAI model using a dataset from the Korean MMORPG, AION, which includes game logs of human players and game bots. More than one classification model has been applied to the dataset to be analyzed by applying interpretable models. This provides us explanations about the game bots' behavior, and the truthfulness of the explanations has been evaluated. Besides, interpretability contributes to minimizing false detection, which imposes unfair restrictions on human players.
