面向 NPC 群体的社会认知架构-Transformer 心智理论与分层强化学习的整合
发布时间：2026-07-07
点击次数：
- 发布时间：
- 2026-07-07
- 影响因子：
- 18.8
- 论文名称：
- 面向 NPC 群体的社会认知架构-Transformer 心智理论与分层强化学习的整合
- 发表刊物：
- Artificial Intelligence Review
- 摘要：
- Non-Player Characters (NPCs) require social cognition to enable intelligent and interactive behaviours within virtual environments. In gaming and other multi-agent systems, current NPC models often fall short in social intelligence and coordination because they cannot infer or anticipate the mental states of other agents. To address this gap, this paper introduces a novel social cognitive architecture that integrates Hierarchical Reinforcement Learning (HRL) with a Transformer-based Theory of Mind (ToM). The framework enables NPCs to make goal-oriented decisions while simultaneously inferring and adapting to the beliefs, goals, and intentions of others. By combining the social awareness capabilities of the ToM module with the strategic decision-making strength of HRL, NPCs are able to coordinate actions more effectively and respond dynamically to evolving scenarios. On a held-out test split of the MCPDial dialogue dataset, the proposed architecture achieves 96.33% accuracy in predicting NPCs’ next action and inferred mental state from dialogue context, a task-specific measure of next-turn prediction performance rather than a general-purpose Theory-of-Mind benchmark, representing a 5.35% improvement over baseline HRL models without ToM (91.96%) on the same task Further enhancements are observed in agent coordination and dialogue coherence. Overall, this approach advances both task-driven reasoning and social interaction in complex multi-agent environments, marking a significant step toward more intelligent and socially aware NPCs.
- 第一作者：
- QIU Zhenyu
- 论文类型：
- 期刊论文
- 通讯作者：
- TANG Xiaojun
- 学科门类：
- 工学
- 文献类型：
- J
- 卷号：
- 59
- 期号：
- 239
- 是否译文：
- 否
- 发表时间：
- 2026-07-07
- 收录刊物：
- SCI