A Multi-Agent Framework for Enhanced Detection of Player Behavior in MMORPGS

Automated bots in MMORPGs pose a significant threat to fair play by exploiting game mechanics and disrupting in-game economies. Traditional detection methods, reliant on rule-based heuristics and signature matching, struggle to keep pace with the adaptive behaviors of modern bots. In response, this paper presents a novel, multi-agent framework designed to model player behavior in a contextual and dynamic manner. The framework integrates structured knowledge graphs with AI-driven semantic search, representing gameplay interactions within a high-dimensional graph. This enables the use of vector embeddings and similarity search for efficient similarity retrieval and real-time anomaly detection. At the heart of this framework is a collaborative multiagent system, where each agent specializes in a distinct facet of player behavior. A Large Language Model (LLM) plays a pivotal role, refining anomaly detection by assessing deviations from human-like actions and reasoning. This provides nuanced insights into potential bot activities and enhances the system's adaptability against evolving bot tactics. Through the synthesis of information across these agents, the framework achieves self-learning capabilities and scalability. Ultimately, this approach significantly improves bot detection accuracy while preserving the integrity and fluidity of Massively Multiplayer Online Role-Playing Game (MMORPG) environments, ensuring a more equitable gaming experience for all players.