The exploration of large language models (LLMs) for task planning and IoT automation has recently gained significant attention. However, existing works suffer from limitations in terms of resource accessibility, complex task planning, and efficiency. In this paper, we present LLMind, an LLM-based AI agent framework that enables effective collaboration among IoT devices for executing complex tasks. Inspired by the functional specialization theory of the brain, our framework integrates an LLM with domain-specific AI modules, enhancing its capabilities. Complex tasks, which may involve collaborations of multiple domain-specific AI modules and IoT devices, are executed through a control script generated by the LLM using a Language-Code transformation approach, which first converts language descriptions to an intermediate finite-state machine (FSM) before final precise transformation to code. Furthermore, the framework incorporates a novel experience accumulation mechanism to enhance response speed and effectiveness, allowing the framework to evolve and become progressively sophisticated through continuing user and machine interactions.

翻译：近年来，利用大型语言模型（LLM）进行任务规划与物联网自动化的探索备受关注。然而现有方法在资源可访问性、复杂任务规划及执行效率方面仍存在局限。本文提出LLMind——一种基于LLM的AI智能体框架，能够实现物联网设备间的高效协作以完成复杂任务。受大脑功能特化理论启发，该框架将LLM与领域专用AI模块深度融合，显著增强其任务处理能力。针对需多领域AI模块与物联网设备协作的复杂任务，框架采用语言-代码转换方法生成控制脚本：首先将自然语言描述转化为中间状态有限状态机（FSM），再精确转换为可执行代码。此外，框架创新性引入经验积累机制，通过持续的用户与机器交互实现动态演进，有效提升响应速度与任务执行效能。