CIF: Small: Distributed Online Decision-Making in Large-Scale Networks

CIF：小型：大型网络中的分布式在线决策

• 批准号: 1261120
• 负责人: Maxim Raginsky
• 金额: $ 40.81万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-16 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261120&HistoricalAwards=false
• 关键词: CIF; Small; Distributed; Online; Decision

Decentralized architectures for decision-making are coming to the fore as more and more distributed infrastructures are deployed to address current challenges and needs of society, including "smart grids" for energy distribution, sensor and actuator networks for ecological monitoring and control, sustainable mass transportation systems, etc. The objective is to ensure that local actions at the nodes result in coherent collective behavior of the network. Systems of this type operate in highly uncertain environments that are noisy, unpredictable and possibly subject to adversarial disturbances. The goal of this research project is to develop a comprehensive theoretical and algorithmic framework for real-time adaptive decision-making in such large-scale systems under resource and cost constraints.Online decision-making is concerned with real-time sequential planning in the presence of model uncertainty, nonstationarity, and possibly adversarial disturbances. The investigators study a novel extension of this paradigm to decentralized settings, where the actions have to be taken at the nodes of a large network, and the nodes only have access to noisy local information. The research entails explicit consideration of a temporally varying environment with a priori unknown dynamics; analysis and application in settings with significant model uncertainty, potential unmodeled statistical dependencies in observations either across the network nodes or over time, and possible adversarial contamination of data; and accounting for the influence of the decisions and network actions on the surrounding environment. The theoretical component of the project captures the impact of decentralization on the quality of the decision-making; the algorithmic component is to develop, analyze, and implement algorithms that come as close as possible to the derived theoretical bounds.

随着越来越多的分布式基础架构被部署，以应对社会的当前挑战和需求，包括用于能源分配的“智能电网”，用于生态监测和控制的“智能电网”，可持续的大众运输系统等。目的是确保目标是确保在NODES的当地行动中，该目标是在CONERTIVE CONTRATIVE COLTATIVE COLTATION COLTATION CORTATION。这种类型的系统在高度不确定的环境中运行，这些环境嘈杂，不可预测且可能受到对抗性干扰的影响。该研究项目的目的是在资源和成本约束下，在此类大规模系统中为实时自适应决策制定全面的理论和算法框架。在线决策与实时的顺序计划有关，在存在模型不确定性，非机构性和可能的​​对抗性障碍的情况下。研究人员研究了该范式到分散设置的新型扩展，在该设置中必须在大型网络的节点上采取行动，而节点只能访问嘈杂的本地信息。该研究需要明确考虑更加变化的环境，并具有先验的未知动态。在具有重要模型不确定性的设置中的分析和应用，在整个网络节点或随着时间的推移中的观测值中潜在的未建模统计依赖性，以及可能对数据的对抗性污染；并考虑决策和网络行动对周围环境的影响。该项目的理论组成部分捕获了权力中心化对决策质量的影响；算法组件是开发，分析和实施与衍生理论界限尽可能接近的算法。