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.

You are free to share this article under the Attribution 4.0 International license. U. TEXAS – AUSTIN (US) -- An artificially intelligent virtual gamer has won the BotPrize by convincing a panel of judges that it was more human-like than half of its human opponents. The competition was sponsored by 2K Games and was set inside the virtual world of "Unreal Tournament 2004," a first-person shooter video game. "The idea is to evaluate how we can make game bots, which are non-player characters (NPCs) controlled by AI algorithms, appear as human as possible," says Risto Miikkulainen, professor of computer science at the University of Texas at Austin. Miikkulainen created the bot, called the UT 2 game bot, with doctoral students Jacob Schrum and Igor Karpov.