Paper Info
Title
XAI-Driven Explainable Multi-view Game Cheating Detection
Abstract
Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.
Year
DOI
Venue
2020
10.1109/CoG47356.2020.9231843
2020 IEEE Conference on Games (CoG)
Keywords
DocType
ISSN
explainable artificial intelligence,cheating detection,online game,industrial application
Conference
2325-4270
ISBN
Citations 
PageRank 
978-1-7281-4534-1
0
0.34
References 
Authors
9
7
Name
Order
Citations
PageRank
Jianrong Tao15111.96
Yu Xiong200.34
Shiwei Zhao301.35
Yuhong Xu400.34
Jianshi Lin500.34
Runze Wu613.05
Changjie Fan75721.37