让AI代理在元宇宙中更懂人类情绪，提升服务真实感
An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem
- 通过可解释的情绪对齐框架，融合事实与情感决策
- 在离线点餐场景中实现更自然的社会交互行为
- 适合研究元宇宙智能体、人机情感交互的开发者
元宇宙服务是元宇宙与服务系统融合的产物，旨在解决数字人、数字孪生体和数字原住民相关的服务挑战。随着大语言模型（LLM）的发展，代理在元宇宙服务生态中扮演双重角色：既作为用户在虚拟世界的数字化身，也作为提供个性化支持的服务助手（或非玩家角色）。然而，现有基于LLM的代理在构建元宇宙服务生态系统时，难以弥合虚拟世界与现实世界服务之间的鸿沟，面临角色数据融合、角色知识关联及伦理安全等难题。本文提出一种面向元宇宙服务生态中基于LLM的代理的可解释情绪对齐框架，旨在将事实因素系统性地融入代理决策流程，实现更精准的关系事实对齐。最后，在离线到离线餐饮配送场景中进行模拟实验，验证了该框架的有效性，实现了更真实的社交涌现现象。
原文摘要 · Abstract (English)
Metaverse service is a product of the convergence between Metaverse and service systems, designed to address service-related challenges concerning digital avatars, digital twins, and digital natives within Metaverse. With the rise of large language models (LLMs), agents now play a pivotal role in Metaverse service ecosystem, serving dual functions: as digital avatars representing users in the virtual realm and as service assistants (or NPCs) providing personalized support. However, during the modeling of Metaverse service ecosystems, existing LLM-based agents face significant challenges in bridging virtual-world services with real-world services, particularly regarding issues such as character data fusion, character knowledge association, and ethical safety concerns. This paper proposes an explainable emotion alignment framework for LLM-based agents in Metaverse Service Ecosystem. It aims to integrate factual factors into the decision-making loop of LLM-based agents, systematically demonstrating how to achieve more relational fact alignment for these agents. Finally, a simulation experiment in the Offline-to-Offline food delivery scenario is conducted to evaluate the effectiveness of this framework, obtaining more realistic social emergence.
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