Active Regression for Cyber-Physical Systems

Abstract:

One defining feature of cyberphysical systems is the fact that human users are closely intertwined with the physical system. Yet both the system and users themselves are often do not explicitly know how users would behave. A natural question arises: How do we design cyberphysical systems that effectively learn about their users, and optimize system behavior accordingly? This poster presents the idea of active regression as a vehicle to learn about users. We show that active learning can yield significant gain over passive learning, and we show that a simple threshold algorithm can achieve the optimal gain.