Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Social cognitive architecture for NPC groups: integration of transformer theory of mind and hierarchical reinforcement learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

查看全文
  • Zhenyu Qiu,
  • Xiaojun Tang

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.