@inproceedings{91493815c09246e08d0a1653d5d11c21,
title = "Understand watchdogs: Discover how game bot get discovered",
abstract = "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.",
keywords = "Explainable artificial intelligence, Game bot detection",
author = "Eunji Park and Park, {Kyung Ho} and Kim, {Huy Kang}",
note = "Publisher Copyright: {\textcopyright} 2021 by SCITEPRESS - Science and Technology Publications, Lda.; 13th International Conference on Agents and Artificial Intelligence, ICAART 2021 ; Conference date: 04-02-2021 Through 06-02-2021",
year = "2021",
language = "English",
series = "ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence",
publisher = "SciTePress",
pages = "924--931",
editor = "Rocha, {Ana Paula} and Luc Steels and {van den Herik}, Jaap",
booktitle = "ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence",
}