Towards a General Automatic Reasoning Framework for Networked Systems

ECCS-1002258戴怀玉北卡罗莱纳州立大学面向网络系统的通用自动推理框架摘要知识产权：本研究的目标是开发一个通用的和灵活的框架，在网络系统中的自动推理，可以接受广泛的应用，并提供所需的准确性和效率之间的权衡，同时允许简单和分布式的实现。鉴于精确干扰一般是棘手的，而广泛采用的平均场方法是有限的推理质量，本研究的方法是探索结构化变分方法，以弥合差距，并允许灵活的折衷之间的各种潜在的冲突的目标，这可以根据应用程序的需求和可用资源进行调整。各种模型，依赖结构和推理算法将被认为是在这个变分推理框架，以及高效的分布式实现。一个相关的研究路线是设计自动聚类方案，理想情况下，可以匹配推理任务的需求，同时易于实现分布式方式。将探索聚类和推理层之间的交互，以实现更好的性能和自适应性。更广泛的影响：该项目有助于基本概念，设计，分析工具和软件，以建立一个通用的和系统的框架，在大规模的网络系统，如传感器网络和网络物理系统的自动推理。预期的结果可以应用于不同的领域，包括安全，交通，医药，能源，制造业和许多其他领域。通过适当的修改，本文提出的分布式聚类算法在网络科学与工程的各个领域也有着广泛的应用。拟议的研究的多学科性质也有助于跨学科的教育和未来的IT劳动力的全面培训。为鼓励代表性不足群体的学生参与，作出了特别努力。通过各种渠道向工业界和广大公众传播研究成果、开发的软件和教育材料。

ECCS-1002258Huaiyu DaiNorth Carolina State UniversityTowards a General Automatic Reasoning Framework for Networked Systems ABSTRACTIntellectual Merit: The objective of this research is to develop a general and flexible framework for automatic inference in networked systems that can admit wide applications and provide desired tradeoff between accuracy and efficiency, while allowing simple and distributed implementation. Given that exact interference is generally intractable whereas the widely-adopted mean field approach is limited in inference quality, the approach of this research is to explore the structured variational methods to bridge the gap and allow flexible tradeoff among various potentially conflicting goals, which can be tuned according to the needs and available resources of applications. Various models, dependency structures, and inference algorithms will be considered in this variational inference framework, together with efficient distributed implementation. An associated research line is to design automatic clustering schemes that ideally can match the needs of inference tasks while being easy to implement in a distributed manner. The interaction between the clustering and inference layer will be explored to achieve better performance and adaptivity. Broader Impacts: This project contributes fundamental concepts, designs, analytical tools and software towards building a general and systematic framework for automatic reasoning in large-scale networked systems, such as sensor networks and cyber-physical systems. The expected outcome can be applied in diverse areas including security, transportation, medicine, energy, manufacturing and many others. Through suitable modification, the proposed distributed clustering algorithms also admit wide applications in various areas of network science and engineering. The multi-disciplinary nature of the proposed research also lends itself to cross-disciplinary education and well-rounded training of a future IT workforce. Special efforts are taken to encourage the participation of students from underrepresented groups. Various channels are pursued to disseminate research findings, developed software, and educational materials to industry and the broader public.