论文标题
学习在集体自适应系统中学习：用于数据驱动推理的采矿设计模式
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning
论文作者
论文摘要
具有学习能力的工程集体自适应系统（CAS）由于其多维且复杂的设计空间，是一项具有挑战性的任务。数据驱动的CAS设计方法可能会引入新的见解，从而使系统工程师能够在设计相的成本效益上更加具有成本效益。本文介绍了一种系统的方法来推理有关基于学习的CAS的设计选择和模式。使用来自系统文献综述的数据，通过新颖的数据驱动方法（例如聚类，多个对应分析和决策树）进行推理。展示了基于过去的经验以及支持小说和创新设计选择的推理。
Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multi-dimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.
