合成语言智能体：具身化有限生命智能体如何通过具有因果关系的社会经验学习语言可供性

中文摘要 AI 辅助

当代语言模型能够流畅对话并影响人类决策，但其交互并未融入自身持续且脆弱的生命过程。语言智能体理论将这种缺失的关联定义为语言智能体，其特征包括具身性、语言参与性与脆弱性：即一个会行动并承担后果的身体、一种能同时改变智能体与交互对象的互动，以及一个可维持或丧失的未来。两项协同研究探讨该组织如何在人工系统中实现：首先，我们将这些关联转化为可检验的合成语言智能体（SLA）标准，并识别出若干现有SLA系统；其次，基于稳态调节强化学习，我们开发了一种以死亡为基础的语言强化学习模型，并将其实例化为具身化有限生命智能体（EMA）。EMA学习到说话方式会改变交互对象保护它的意愿，并通过考量这些回应对其剩余生命的意义来选择表达。控制实验表明，语言选择取决于EMA的身体状态与社会历史，会改变交互对象的行为，并通过与特定对象的互动实现适应。当身体后果持续存在时，语言选择会改变同一生命的未来；当身体重置时，其社会效应仍存在，但不再塑造持续生存的能力。最终的EMA在我们的操作定义下展现出SLA。本研究推动合成共情与战略性人机交互的进一步研究：即具有持续身体、历史与未来的人工智能体如何发展并表达共情，以及人类如何关怀、协商或管控它们。

英文摘要

Contemporary language models can converse fluently and influence human decisions, yet their exchanges do not enter a continuing, vulnerable life of their own. Linguistic-agency theory identifies this missing connection as linguistic agency and characterizes it through embodiment, linguistic participation, and precariousness: a body that acts and bears consequences, interaction that changes both agent and partner, and a future that can be sustained or lost. Two coordinated studies examine how this organization can appear in artificial systems. First, we translate these relations into inspectable criteria for Synthetic Linguistic Agency (SLA) and identify several existing SLA systems. Second, building on Homeostatically Regulated Reinforcement Learning, we develop a mortality-grounded linguistic-reinforcement-learning model and instantiate it in an Embodied Mortal Agent (EMA). The EMA learns how ways of speaking change a partner's willingness to protect it and chooses expressions by considering what those responses mean for its remaining life. Controlled experiments show that linguistic choices depend on the EMA's body and social history, change partner behavior, and adapt through experience with particular partners. When bodily consequences persist, linguistic choices alter the future of the same life; when the body is reset, their social effects remain but no longer shape continued viability. The resulting EMA exhibits SLA under our operational definition. This work motivates further research on synthetic empathy and strategic human-AI interaction: how artificial agents with persistent bodies, histories, and futures might develop and express empathy, and how people might care for, negotiate with, or govern them.

发表机构

• College of Engineering, Shantou University（汕头大学工程学院）
• Department of Computer Science, Shantou University（汕头大学计算机科学系）

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