对话式群智感知：一种平行智能驱动的新型感知方法
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
AI总结：
本文提出面向工业5.0的对话式群智感知范式，通过平行智能与LLM驱动的人机AI多级对话架构，有效组织生物、机器人和数字参与者，降低参与门槛并提升系统响应速度。
摘要：
从基于CPS的工业4.0向基于CPSS的工业5.0的过渡，为当前的感知方法带来了新的要求和机遇，特别是考虑到Chatbots和大型语言模型（LLMs）的最新进展。因此，我们见证了平行智能驱动的群智感知智能（CSI）的进步，目前正朝着语言智能方向发展。在本文中，我们提出了一种面向工业5.0的新型感知范式，即对话式群智感知。它可以减轻个人的工作量和专业要求，促进多样化劳动力的组织和运作，从而加快群智感知系统的响应速度并扩大其普及范围。具体而言，我们设计了对话式群智感知的架构，以有效组织来自不同社区的三类参与者（生物、机器人和数字）。通过三个层次的有效对话（即人与人、人与AI、AI与AI），可以实现不同工作者的复杂交互和服务功能，以完成三个感知阶段（即请求、调度和执行）的各种任务。此外，我们探索了实现对话式群智感知的基础技术，包括基于LLM的多智能体系统、场景工程和对话式人机合作。最后，我们介绍了对话式群智感知的潜在工业应用并讨论了其影响。我们设想，自然语言对话将成为群智感知过程中的主要通信渠道，实现人类、机器人和AI之间更丰富的信息交换和协作问题解决。
英文摘要：
The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advancement of parallel intelligence-powered Crowdsensing Intelligence (CSI) is witnessed, which is currently advancing towards linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0. It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential industrial applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
