LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution
CoRR(2023)
摘要
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.
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