{"title":"LLMind:利用 LLM 协调人工智能和物联网以执行复杂任务","authors":"Hongwei Cui, Yuyang Du, Qun Yang, Yulin Shao, Soung Chang Liew","doi":"arxiv-2312.09007","DOIUrl":null,"url":null,"abstract":"In this article, we introduce LLMind, an innovative AI framework that\nutilizes large language models (LLMs) as a central orchestrator. The framework\nintegrates LLMs with domain-specific AI modules, enabling IoT devices to\ncollaborate effectively in executing complex tasks. The LLM performs planning\nand generates control scripts using a reliable and precise language-code\ntransformation approach based on finite state machines (FSMs). The LLM engages\nin natural conversations with users, employing role-playing techniques to\ngenerate contextually appropriate responses. Additionally, users can interact\neasily with the AI agent via a user-friendly social media platform. The\nframework also incorporates semantic analysis and response optimization\ntechniques to enhance speed and effectiveness. Ultimately, this framework is\ndesigned not only to innovate IoT device control and enrich user experiences\nbut also to foster an intelligent and integrated IoT device ecosystem that\nevolves and becomes more sophisticated through continuing user and machine\ninteractions.","PeriodicalId":501433,"journal":{"name":"arXiv - CS - Information Theory","volume":"28 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LLMind: Orchestrating AI and IoT with LLMs for Complex Task Execution\",\"authors\":\"Hongwei Cui, Yuyang Du, Qun Yang, Yulin Shao, Soung Chang Liew\",\"doi\":\"arxiv-2312.09007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, we introduce LLMind, an innovative AI framework that\\nutilizes large language models (LLMs) as a central orchestrator. The framework\\nintegrates LLMs with domain-specific AI modules, enabling IoT devices to\\ncollaborate effectively in executing complex tasks. The LLM performs planning\\nand generates control scripts using a reliable and precise language-code\\ntransformation approach based on finite state machines (FSMs). The LLM engages\\nin natural conversations with users, employing role-playing techniques to\\ngenerate contextually appropriate responses. Additionally, users can interact\\neasily with the AI agent via a user-friendly social media platform. The\\nframework also incorporates semantic analysis and response optimization\\ntechniques to enhance speed and effectiveness. Ultimately, this framework is\\ndesigned not only to innovate IoT device control and enrich user experiences\\nbut also to foster an intelligent and integrated IoT device ecosystem that\\nevolves and becomes more sophisticated through continuing user and machine\\ninteractions.\",\"PeriodicalId\":501433,\"journal\":{\"name\":\"arXiv - CS - Information Theory\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Information Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2312.09007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Information Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2312.09007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
LLMind: Orchestrating AI and IoT with LLMs for Complex Task Execution
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.