在集体自适应系统中学会学习：数据驱动推理的设计模式挖掘
Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning
摘要：
具有学习能力的集体自适应系统（CAS）是一项具有挑战性的任务，由于其多维和复杂的设计空间。CAS设计的数据驱动方法可以引入新的见解，使系统工程师能够在设计阶段更经济有效地管理CAS复杂性。本文介绍了一个系统的方法来推理的设计选择和模式的学习型CAS。使用系统的文献综述的数据，推理与数据驱动的方法，如聚类，多重对应分析和决策树的新应用程序进行。论证了基于过去经验的推理以及支持新颖和创新的设计选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
