Online machine learning (ML) is often used in self-adaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties -- traps -- that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on self-adaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.

翻译：在线机器学习（ML）常被用于自我自适应系统中，以强化自适应机制并提升系统效用。尽管具有这些优势，但将在线机器学习应用于自我自适应可能面临挑战，目前报告其局限性的论文并不多见。近期，我们在智能农业场景中尝试将在线机器学习用于自我自适应，遇到了若干意料之外的困难——即陷阱——据我们所知，这些陷阱在相关学术社区中尚未得到充分讨论。本文旨在报告我们应对这些陷阱的经验。具体而言，我们讨论了与基于机器学习的估计器规范及在线训练相关的若干陷阱、这些陷阱对自我自适应的影响，以及用于评估估计器的方法。通过对这些陷阱的概述，我们总结了一系列经验教训，可作为其他研究人员和实践者在将在线机器学习应用于自我自适应时的指导参考。