Safe learning with real-time constraints: A case study

Abstract

Aim of this work is to study the problem of ensuring safety and effectiveness of a multi-agent robot control system with real-time constraints in the case of learning components usage. Our case study focuses on a robot playing the air hockey game against a human opponent, where the robot has to learn how to minimize opponent's goals. This case study is paradigmatic since the robot must act in real-time, but, at the same time, it must learn and guarantee that the control system is safe throughout the process. We propose a solution using automata-theoretic formalisms and associated verification tools, showing experimentally that our approach can yield safety without heavily compromising effectiveness. © 2010 Springer-Verlag.