Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adaptation rules is often a challenge because it essentially involves searching the optimal design in a complex (variables) space. In response, this paper attempt to employ large language models (LLMs) as a optimizer to construct and optimize adaptation rules, leveraging the common sense and reasoning capabilities inherent in LLMs. Preliminary experiments conducted in SWIM have validated the effectiveness and limitation of our method.

翻译：基于规则的适应是自适应领域的一种基础方法，以其人类可读性和快速响应为特征。然而，构建高性能且鲁棒的适应规则通常是一项挑战，因为这本质上涉及在复杂的（变量）空间中搜索最优设计。为此，本文尝试利用大语言模型（LLMs）作为优化器来构建和优化适应规则，以利用LLMs固有的常识和推理能力。在SWIM中进行的初步实验验证了我们方法的有效性和局限性。