Constructing Hybrid-Driven Agents for MOBA through Player Modeling: Integrating Behavioral Analysis and Narrative Persona in Human-Centered AI

Abstract

To address the challenges of rigid behavioral patterns, lack of semantic consistency, and insufficient emotional interaction in current MOBA game AI agents, this study proposes a Persona-Driven Game AI Agents (PDGAIA) framework based on the Human-Centered AI (HCAI). The framework integrates Dual-Channel Player Profiling, a Narrative Consistency Engine, and Hierarchical Reinforcement Learning (HRL) to enhance the personalization, immersion, and tactical adaptability of game AI. Using Tencent’s MOBA game Honor of Kings as a case study, player personas are constructed from large-scale match logs and survey data. By combining objective behavioral indicators and subjective psychological modeling, core player personality dimensions and their tactical preferences are identified. Based on narrative strategies, the player-modeled character prototypes are transformed into AI agents with consistent personas, leveraging Hunyuan LLMs for multimodal expression. An HRL framework is then employed to decouple tactical decision-making from action execution, enabling the development of stylized AI combat systems. This framework successfully ensures a coherent persona expression across combat behavior, voice tone, and tactical communication. Experimental results demonstrate that the dual-path approach, which combines data-driven modeling and narrative persona construction, significantly improves anthropomorphism and social presence, delivering a more immersive and intelligent gaming experience. The proposed framework provides a reusable HCI methodology for constructing Game AI agents.

Keywords: MOBA games, player modeling, Hierarchical Reinforcement Learning (HRL), narrative persona, human-centered AI

DOI: 10.54941/ahfe1006246