no code implementations • NeurIPS 2020 • Simon Zhuang, Dylan Hadfield-Menell
We consider the cost of this incompleteness by analyzing a model of a principal and an agent in a resource constrained world where the $L$ attributes of the state correspond to different sources of utility for the principal.
no code implementations • 29 Dec 2020 • Arnaud Fickinger, Simon Zhuang, Andrew Critch, Dylan Hadfield-Menell, Stuart Russell
We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory, Gibbard's theorem, by using a sufficiently collegial preference inference mechanism.
no code implementations • 19 Jul 2020 • Arnaud Fickinger, Simon Zhuang, Dylan Hadfield-Menell, Stuart Russell
Assistance games (also known as cooperative inverse reinforcement learning games) have been proposed as a model for beneficial AI, wherein a robotic agent must act on behalf of a human principal but is initially uncertain about the humans payoff function.
