CAREER: Reinforced Imitative Graph Learning: Bridging the Gap between Perception and Prescription in Graph Sequences

在交通网络和电网等关键基础设施的现代管理中，机器智能有望感知和评估情况，跟踪和推断变化，控制和减轻系统威胁。例如，在电网管理中，人工智能可以帮助描述电力分布（发生了什么）和预测未来的电力需求（将发生什么）；在红绿灯控制方面，AI可以模拟红绿灯控制机制下的交通演化（如何变化），生成更好的控制机制（如何改变），实现安全高效的交通。该项目将开发新的人工智能技术，为系统配备感知智能，以了解发生了什么并预测将发生什么，以及处方智能，以了解系统如何变化。研究成果可以帮助计算机更好地评估情况、预测趋势、检测异常、发现因果关系、模拟系统行为，以及预防、减轻和消除对系统的威胁。这种能力对于关键基础设施的操作和防御非常重要。此外，这项研究为本科生、研究生和代表性不足的学生提供了新的课程、研究和实习机会。相互连接的关键基础设施可以看作是产生大图序列数据的动态网络系统。这些数据是感知和处方智力的重要来源。该项目将开发一个变革性框架，将感知和处方概括和统一为一个联合和互动的学习架构。该项目将解决三个基本的研究挑战：(1)如何设计一个统一的学习范式来同时执行图序列中的感知和处方？(2)新的学习范式能否用于开发图序列的精确表示和可靠投影能力？(3)新的学习范式能否用于开发具有交互和反馈能力的系统模拟器和干预计划者？该项目将产生新的算法，包括强化图模仿嵌入、对抗性信心训练、说明性干预以及与外部和因果知识的互动学习。该项目将辅以一项综合评估计划，包括交通网络、电网、社会网络数据。这项研究工作将为预测和可操作知识之间的蒸馏、转移和反馈机制提供新的探索和见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In modern management of critical infrastructures, such as transportation networks and power grids, machine intelligence is expected to perceive and assess situations, track and infer changes, and control and mitigate system’s threats. For example, in power grid management, AI can assist in characterizing the electrical distribution (what happened) and forecasting future electrical demand (what will happen); in traffic light control, AI can simulate traffic evolution under traffic light control mechanisms (how it changes) and generate better control mechanisms (how to change it) for safe and efficient transportation. This project will develop novel AI techniques to equip systems with the perception intelligence to understand what happened and predict what will happen, and the prescription intelligence to understand how the system changes. The research outcomes can help computers to better assess situations, forecast trends, detect anomalies, discover causality, simulate system behaviors, and, moreover, prevent, mitigate, and eliminate threats to the system. Such capabilities are important for the operations and defense of critical infrastructures. Furthermore, this research provides new courses, research, and internship opportunities for undergraduate, graduate, and underrepresented students.The interconnected critical infrastructures can be viewed as dynamic network systems that generate big graph sequence data. Such data is an essential source of the perception and prescription intelligence. This project will develop a transformative framework that generalizes and unifies perception and prescription into a joint and interactive learning architecture. This project will address three fundamental research challenges: (1) How can a unified learning paradigm be designed to simultaneously perform perception and prescription in graph sequences? (2) Can the new learning paradigm be used to develop precise representation and reliable projection capabilities of graph sequences? (3) Can the new learning paradigm be used to develop system simulators and intervention planners with interaction and feedback capabilities? This project will result in new algorithms, including reinforced graph imitative embedding, adversarial confidence training, prescriptive intervention, and interactive learning with external and causal knowledge. The project will be complemented by a comprehensive evaluation plan with transportation networks, power grids, social networks data. This research effort will provide new exploration and insights into distillation, transfer, and feedback mechanism between predictive and actionable knowledge.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.