Towards an Intermittent Learning Framework for Smart and Efficient Cyber-Physical Autonomy

Current learning algorithms cannot be easily applied in CPS due to their need for continuous and expensive updates, with the current triggered frameworks having fundamental limitations. Such limitations lead to the following questions. How can we incorporate and fully adapt to totally unknown, dynamic, and uncertain environments? How do we co-design the action and the intermittent schemes? How can we provide quantifiable real-time performance, stability and robustness guarantees by design? And how do we solve congestion and guarantee security? We will build on our multiyear experience and develop fundamental contributions to CPS by providing novel frameworks that will allow the fully autonomous operation in the face of unknown, bandwidth restricted, and adversarial environments.

As specific merits, the project will, (i) unify new perspectives of learning in engineering to enable smart autonomy, resiliency, bandwidth efficiency, robustness, real-time optimality and adaptation that cannot be achieved with the state-of-the-art approaches; (ii) develop intermittent deep learning methods for CPS that can mitigate sensor attacks by dynamically isolating the suspicious components and can handle cases of limited sensing capabilities; (iii) incorporate nonequilibrium game-theoretic learning in CPS with components that do not share similar mechanisms for decision making and do not have the same level of rationality due to heterogeneity and may differ in either their information obtained or their ability in utilizing it; and (iv) investigate ways to transfer intermittent learning experiences among the agents.