Automatic Adaptation Rule Optimization via Large Language Models
<|reference_start|>Automatic Adaptation Rule Optimization via Large Language Models: 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.<|reference_end|>
@article{ishimizu2024automatic,
    title={Automatic Adaptation Rule Optimization via Large Language Models},
    author={Yusei Ishimizu, Jialong Li, Jinglue Xu, Jinyu Cai, Hitoshi Iba, Kenji
  Tei},
    journal={arXiv preprint arXiv:2407.02203},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02203},
    primaryClass={cs.CL cs.AI}
}