CAREER: Toward Artificial General Intelligence for Complex Adaptive Systems: A Natural Concurrent “Learning-in-Learning” Control Paradigm

Artificial intelligence (AI) technologies are transforming nearly every aspect of our lives and reinforcement learning (RL) is viewed as one of next big research topics in the current AI wave. While the existing AI and RL achievements are exciting, the fundamental research of data aggregation, learning and approximation capability, and the performance generalization during uncertainties, is not fully yet developed.

There is still a gap from the current state-of-the-art techniques to the artificial general intelligence that can bring good performance in learning speed, data efficiency, and generalization of the optimization performance.

Inspired by this observation, the PI proposes a natural concurrent RL framework that carries three major advantages over traditional RL methods, namely the i) advantages of simultaneously learning multimodal properties of the complex system; ii) structural advantages of using a personalized learning scheme; and iii) implementation advantages of the data-driven sample-efficient design. Within this framework, the PI proposes to design two concurrent RL methods to consolidate past experiences and anticipatory knowledge and build the “learning-in-learning” control paradigm.

The theoretical results will certify that the proposed RL framework can be deployed with high confidence for complex adaptive systems under uncertain environments. The applications on smart energy community will support the novel learning framework and theoretical results.

Beyond the scientific impacts, the proposed research has broader impacts for a wide range of research disciplines including transportation, rehabilitation, and robotics. The integration of research and education activities will also positively impact the institutions regionally and nationally. A proposed workshop will bring world renown experts to engage (state college) students and young researchers with limited financial supports to attend professional conferences.

The collaboration with the industry and the national laboratory provides the students the opportunity to get external training, which can lead to competitive job offers. The proposed take-home AI/RL projects will promote interactive distance learning for schools with limited research capacity (e.g., rural community college) and for students with the preference of remote studying during the current pandemic. These activities will vigorously contribute to the nation’s AI workforce development.

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.