Human-AI Collaborative Bot Detection in MMORPGs
This work addresses bot detection for MMORPG developers and players to maintain game balance, but it is incremental as it builds on existing unsupervised and AI-assisted methods.
The paper tackled the problem of detecting auto-leveling bots in MMORPGs, which undermine gameplay fairness, by proposing a human-AI collaborative framework that uses unsupervised contrastive learning and clustering with LLM validation, resulting in improved detection efficiency and explainability.
In Massively Multiplayer Online Role-Playing Games (MMORPGs), auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions require explainable justification to avoid legal and user experience issues. In this paper, we present a novel framework for detecting auto-leveling bots by leveraging contrastive representation learning and clustering techniques in a fully unsupervised manner to identify groups of characters with similar level-up patterns. To ensure reliable decisions, we incorporate a Large Language Model (LLM) as an auxiliary reviewer to validate the clustered groups, effectively mimicking a secondary human judgment. We also introduce a growth curve-based visualization to assist both the LLM and human moderators in assessing leveling behavior. This collaborative approach improves the efficiency of bot detection workflows while maintaining explainability, thereby supporting scalable and accountable bot regulation in MMORPGs.