发表机构
IT-University of Copenhagen（哥本哈根信息技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种目标导向智能体AI架构，通过情境对话使非专家完成物理硬件修复，实验显示95%完成率，但物理交互降低了社会存在感与信任。
AI 中文摘要
泛在机器人系统通常缺乏传统的视觉界面，这使得自然语言交互对于维护和修复变得重要。本文提出了一种目标导向的智能体AI架构，使非专家用户能够通过情境对话完成技术性修复任务。该架构将交互前的目标分解、持久状态跟踪、策略性目标管理和实时对话执行分开。我们在一个物理硬件修复任务中，对二十名参与者进行了系统评估。十九名参与者完成了任务，对应95%的完成率。参与者认为该系统有帮助且胜任，并且智能体对对话干扰（如元查询和代码切换）保持稳健。与之前的在线基线相比，物理交互显著降低了感知社会存在感（p=.0005）以及信任和能力（p=.037），而感知帮助性仍然很高。
英文摘要
Ubiquitous robotic systems often lack traditional visual interfaces, making natural language interaction important for maintenance and repair. This paper presents a goal-oriented agentic AI architecture that enables non-expert users to complete technical repair tasks through situated dialogue. The architecture separates pre-interaction goal decomposition, persistent state tracking, strategic goal management, and real-time conversational execution. We evaluated the system in a physical hardware repair task with twenty participants. Nineteen participants completed the task, corresponding to a 95% completion rate. Participants rated the system as helpful and competent, and the agent remained robust to conversational diversions such as meta-queries and code-switching. A comparison with a prior online baseline showed that physical interaction significantly reduced perceived social presence, (p=.0005), and trust and competence, (p=.037), while perceived helpfulness remained high.
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