CS Talk - Benjamin Rosman
Title: On the Impact of Prior Knowledge on Autonomous Agents
Abstract: Any long-lived autonomous agent faced with a changing environment can be made more effective via learning. However, learning how to act is a slow process, which risks exposing the agent to harm. Fortunately, a long-lived agent can ameliorate this problem by abstracting and reusing knowledge gained from prior learning experiences.
In this talk, I will discuss some of the work from our group on knowledge transfer. This will largely take the form of two questions. Firstly, given a set of previously learnt behaviours, what is the optimal way to select the best one to be re-used in a new environment or interaction? Secondly, how can an agent generalise from previous behaviours to solve new tasks in the same environment more quickly and with less risk? These approaches are presented in the context of reinforcement learning, but I will also discuss some preliminary results in extending them to other decision-making paradigms.
Bio: Benjamin Rosman received a Ph.D. degree in Informatics from the University of Edinburgh in 2014. Previously, he obtained an M.Sc. in Artificial Intelligence from the University of Edinburgh, a Bachelor of Science (Honours) in Computer Science from the University of the Witwatersrand, South Africa, and a Bachelor of Science (Honours) in Computational and Applied Mathematics, also from the University of the Witwatersrand.
He is a Senior Researcher in the Mobile Intelligent Autonomous Systems group at the Council for Scientific and Industrial Research (CSIR) in South Africa, and is also a Visiting Lecturer in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand. He is the Chair of the IEEE South African joint chapter of Control Systems, and Robotics and Automation, and is a member of the Technology Knowledge and Leadership (TK&L) Committee within the SAIEE.