Speaker
Description
Numerous autonomous systems in areas such as social robotics, automated driving systems, or drones, are driven by decision-theoretic control models that rely on various machine learning (ML) algorithms for information processing and decision making purposes. Such ML components are susceptible of being attacked by malicious adversaries to alter the decisions made by the system in a negative manner. This is the realm of a relatively recent field of adversarial machine learning whose aim is to robustify ML algorithms against adversarial attacks. In the talk, I shall describe problems and some solutions in relation to incorporating AML algorithms into autonomous systems, with a focus on social robots as applied domain.
Based on various pieces of work with S. Liu, M. Santos, A. Nuñez, M. Chacón, T. Guy and M. Karny.
