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

随着越来越多的分布式基础设施被部署以应对当前的挑战和社会需求，用于决策的分散式架构正在脱颖而出，包括？智能电网？用于能量分配、用于生态监测和控制的传感器和执行器网络、可持续的大众运输系统等。目标是确保节点处的局部行为导致网络的一致集体行为。这种类型的系统在高度不确定的环境中运行，这些环境是嘈杂的，不可预测的，并且可能受到对抗性干扰。本研究项目的目标是在资源和成本约束下，为此类大规模系统的实时自适应决策开发一个全面的理论和算法框架。在线决策涉及模型不确定性，非平稳性和可能的对抗性干扰存在下的实时顺序规划。研究人员研究了这种范式在分散环境中的一种新的扩展，在这种环境中，必须在大型网络的节点上采取行动，并且节点只能访问嘈杂的本地信息。该研究需要明确考虑具有先验未知动态的时变环境;在具有显著模型不确定性的设置中进行分析和应用，在网络节点或随着时间的推移观察到潜在的未建模统计依赖性，以及可能的对抗性数据污染;并考虑决策和网络行为对周围环境的影响。该项目的理论部分捕获了去中心化对决策质量的影响;算法部分是开发，分析和实现尽可能接近理论界限的算法。

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.