用无监督方法识别游戏外挂角色，结合AI与人工判断提升检测可解释性。
Human-AI Collaborative Bot Detection in MMORPGs
- 基于对比学习和聚类，无监督发现相似升级行为的账号群。
- 引入大模型辅助验证聚类结果，提升判断可靠性。
- 通过成长曲线可视化，方便人工与AI共同评估行为异常。
在大型多人在线角色扮演游戏（MMORPGs）中，自动升级外挂利用自动化程序大规模提升角色等级，破坏游戏平衡与公平性。检测此类外挂极具挑战，不仅因其模仿人类行为，更因惩罚措施需具备可解释性，以避免法律与用户体验问题。本文提出一种新型无监督框架，通过对比表示学习与聚类技术，识别具有相似升级模式的角色群体。为确保决策可靠，引入大语言模型（LLM）作为辅助审核者，模拟二次人工判断。同时设计基于成长曲线的可视化工具，协助LLM与人工管理员评估升级行为。该人机协同方法在提升检测效率的同时保障可解释性，支持MMORPG中可扩展且负责任的外挂治理。
原文摘要 · Abstract (English)
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.
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