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

职业：强化模仿图学习：弥合图序列中感知和规定之间的差距

• 批准号: 2045567
• 负责人: Yanjie Fu
• 金额: $ 56.93万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045567&HistoricalAwards=false
• 关键词: CAREER; Reinforced; Imitative; Graph; Learning

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.

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，并控制和减轻系统的威胁。例如，在电网管理中，人工智能可以帮助描述配电（发生了什么）和预测未来的电力需求（将发生什么）;在交通灯控制中，人工智能可以模拟交通灯控制机制下的交通演变（如何改变），并生成更好的控制机制（如何改变），以实现安全高效的交通。该项目将开发新型人工智能技术，为系统配备感知智能以了解发生了什么并预测将要发生什么，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运营和防御非常重要。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被视为生成大图序列数据的动态网络系统。这些数据是感知和处方情报的重要来源。该项目将开发一个变革性框架，将感知和处方归纳并统一为一个联合和互动的学习架构。本计画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例，以同时执行图形序列中的知觉和处方？(2)新的学习范式是否可以用来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器？该项目将产生新的算法，包括强化图形模仿嵌入，对抗性信心训练，规范性干预以及与外部和因果知识的交互式学习。该项目将辅之以一个综合评估计划，包括交通网络、电网、社交网络数据。这项研究工作将为预测性和可操作性知识之间的蒸馏、转移和反馈机制提供新的探索和见解。该奖项反映了NSF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。