CPS: Small: Neuro-Symbolic Learning and Control with High-Level Knowledge Inference

The use of artificial intelligence in cyber-physical systems is limited by challenges such as data availability, task environment complexity, and the need for expressive and interpretable high-level knowledge representations. To address these challenges, this project aims to develop a set of neuro-symbolic learning and control tools by integrating machine learning, control theory, and formal methods. The results are expected to find application across cyber-physical systems such as robotic systems, autonomous systems, and networked cyber-physical systems. Validation in a testbed environments should facilitate safe deployments in real-world physical environments with provable guarantees and robustness against potential adversaries. The research tasks mainly focus on improving the data efficiency, interpretability, scalability, and resiliency of the neuro-symbolic approaches in cyber-physical systems, with special focus on cyber-physical systems where extensive online interactions may not be safe or feasible, but high-level knowledge can be useful in completing tasks in complex and adversarial environments with provable correctness guarantees. Specifically, the neuro-symbolic learning and control algorithms offer the following three key features. Firstly, they incorporate template-free inference of high-level knowledge (e.g., temporal logic formulas) from uncertain data into neuro-symbolic learning to enable data-efficient reinforcement learning with both offline training and online fine-tuning. Secondly, the approaches are capable of achieving scalable and resilient reinforcement learning in multi-agent adversarial environments where high-level knowledge can be inferred for expediting the agents' learning processes. Lastly, the algorithms provide provable guarantees on complex task specifications using neuro-symbolic learning-based adaptive control with unknown dynamics. The results of this research will be shared through a range of educational activities, including undergraduate and graduate courses, as well as research workshops. Additionally, outreach programs will be implemented to engage K-12 students and diverse groups in the scientific and engineering communities and broaden their participation. 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.