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

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.