While traditional optimization and scheduling schemes are designed to meet fixed, predefined system requirements, future systems are moving toward user-driven approaches and personalized services, aiming to achieve high quality-of-experience (QoE) and flexibility. This challenge is particularly pronounced in wireless and digitalized energy networks, where users' requirements have largely not been taken into consideration due to the lack of a common language between users and machines. The emergence of powerful large language models (LLMs) marks a radical departure from traditional system-centric methods into more advanced user-centric approaches by providing a natural communication interface between users and devices. In this paper, for the first time, we introduce a novel architecture for resource scheduling problems by constructing three LLM agents to convert an arbitrary user's voice request (VRQ) into a resource allocation vector. Specifically, we design an LLM intent recognition agent to translate the request into an optimization problem (OP), an LLM OP parameter identification agent, and an LLM OP solving agent. To evaluate system performance, we construct a database of typical VRQs in the context of electric vehicle (EV) charging. As a proof of concept, we primarily use Llama 3 8B. Through testing with different prompt engineering scenarios, the obtained results demonstrate the efficiency of the proposed architecture. The conducted performance analysis allows key insights to be extracted. For instance, having a larger set of candidate OPs to model the real-world problem might degrade the final performance because of a higher recognition/OP classification noise level. All results and codes are open source.

翻译：尽管传统的优化与调度方案旨在满足固定、预定义的系统需求，但未来系统正朝着用户驱动的方法和个性化服务方向发展，力求实现高质量体验与灵活性。这一挑战在无线和数字化能源网络中尤为突出，由于用户与机器之间缺乏通用语言，用户需求在很大程度上未被纳入考量。强大大型语言模型的出现标志着从传统以系统为中心的方法向更先进的以用户为中心的方法的根本转变，它为用户与设备之间提供了自然的通信接口。本文首次针对资源调度问题提出一种新颖的架构，通过构建三个LLM智能体将任意用户的语音请求转化为资源分配向量。具体而言，我们设计了LLM意图识别智能体以将请求转化为优化问题、LLM优化问题参数识别智能体以及LLM优化问题求解智能体。为评估系统性能，我们在电动汽车充电场景下构建了典型语音请求数据库。作为概念验证，我们主要使用Llama 3 8B模型。通过在不同提示工程场景下的测试，所得结果证明了所提架构的有效性。进行的性能分析可提取关键见解，例如：使用更大的候选优化问题集合来建模现实问题可能会因更高的识别/优化问题分类噪声水平而导致最终性能下降。所有结果与代码均已开源。