Maxime Bouton
I am an AI Researcher at Ericsson Research. My research interests lie in reinforcement learning, planning under uncertainty, and AI safety. Prior to joining Ericsson I completed my PhD at Stanford University under the supervision of Prof. Mykel Kochenderfer. My thesis was about safe and scalable planning under uncertainty for autonomous driving and all the code was written in Julia. At Juliacon 2021 I will present work I did during my thesis.
Session
Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. Model checking allows us to synthesize a decision policy that satisfies a linear temporal logic (LTL) formula in a POMDP. By reformulating the model checking problem into an AI planning problem, we can use state-of-the-art POMDP planning algorithms to solve model checking problems.