LLMind: Orchestrating AI and IoT with LLMs for Complex Task Execution

Hongwei Cui, Yuyang Du, Qun Yang, Yulin Shao, Soung Chang Liew
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Abstract

In this article, we introduce LLMind, an innovative AI framework that utilizes large language models (LLMs) as a central orchestrator. The framework integrates LLMs with domain-specific AI modules, enabling IoT devices to collaborate effectively in executing complex tasks. The LLM performs planning and generates control scripts using a reliable and precise language-code transformation approach based on finite state machines (FSMs). The LLM engages in natural conversations with users, employing role-playing techniques to generate contextually appropriate responses. Additionally, users can interact easily with the AI agent via a user-friendly social media platform. The framework also incorporates semantic analysis and response optimization techniques to enhance speed and effectiveness. Ultimately, this framework is designed not only to innovate IoT device control and enrich user experiences but also to foster an intelligent and integrated IoT device ecosystem that evolves and becomes more sophisticated through continuing user and machine interactions.
LLMind:利用 LLM 协调人工智能和物联网以执行复杂任务
本文介绍的 LLMind 是一种创新型人工智能框架,它利用大型语言模型 (LLM) 作为中心协调器。该框架将 LLM 与特定领域的人工智能模块集成在一起,使物联网设备能够在执行复杂任务时进行有效协作。LLM 使用基于有限状态机(FSM)的可靠而精确的语言代码转换方法执行规划并生成控制脚本。LLM 与用户进行自然对话,采用角色扮演技术生成与上下文相适应的响应。此外,用户还可以通过用户友好型社交媒体平台与人工智能代理轻松互动。该框架还结合了语义分析和响应优化技术,以提高速度和效率。最终,该框架不仅旨在创新物联网设备控制和丰富用户体验,还旨在促进智能集成物联网设备生态系统的发展,并通过持续的用户和机器互动变得更加复杂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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