Algorithm selection and hyperparameter tuning are critical steps in both
academic and applied machine learning. On the other hand, these steps are
becoming ever increasingly delicate due to the extensive rise in the number,
diversity, and distributedness of machine learning resources. Multi-agent
systems, when applied to the design of machine learning platforms, bring about
several distinctive characteristics such as scalability, flexibility, and
robustness, just to name a few. This paper proposes a fully automatic and
collaborative agent-based mechanism for selecting distributedly organized
machine learning algorithms and simultaneously tuning their hyperparameters.
Our method builds upon an existing agent-based hierarchical machine-learning
platform and augments its query structure to support the aforementioned
functionalities without being limited to specific learning, selection, and
tuning mechanisms. We have conducted theoretical assessments, formal
verification, and analytical study to demonstrate the correctness, resource
utilization, and computational efficiency of our technique. According to the
results, our solution is totally correct and exhibits linear time and space
complexity in relation to the size of available resources. To provide concrete
examples of how the proposed methodologies can effectively adapt and perform
across a range of algorithmic options and datasets, we have also conducted a
series of experiments using a system comprised of 24 algorithms and 9 datasets.

该研究论文提出了一种全自动、协作的基于代理机制的方法，用于选择分布式组织的机器学习算法并同时调整其超参数，以实现扩展性、灵活性和鲁棒性。通过理论评估、形式验证和分析研究，证明了该技术的正确性、资源利用和计算效率。在对 24 种算法和 9 个数据集进行实验的基础上，提供了具体示例来展示所提出的方法在不同算法选项和数据集上的有效适应性和性能表现。