AI is increasingly deployed in the industry. It is currently used for applications where decisions are not safety-critical or where human operators can vet the decisions before their deployment. Industries dealing with cyber-physical systems evolving in complex environments could substantially benefit from AI tools that can learn to improve the decisions process using data collected in the past. High-tech companies commonly use Model Predictive Control (MPC) to deal with control and decision problems involving safety requirements, and have started using AI tools for cyber-physical systems at the Research and Development level. Reinforcement Learning (RL), a subfield of AI capable of learning to take optimal decisions for cyber-physical systems, is a very common choice. Unfortunately, deploying RL is problematic whenever safety requirements and liabilities are at stake. Industries want to understand and have safety certificates on the automated decisions driving their products, and this is difficult to obtain for existing RL methods. Hence deploying RL tools in systems involving safety requirements is currently a major difficulty. Some companies involved in Autonomous Driving use ad hoc heuristics to deal with the problem, but a genuine solution is still missing. This project will merge theoretical results from RL with advanced, formal control methods resulting from the field of MPC to create a novel form of AI for cyber-physical systems where the decisions can be explained and certified for safety. Performing the research proposed in this project requires a unique combination of in-depth knowledge both in RL and MPC, which few groups possess. NTNU is currently in a great position to carry this research forward. The project will be integrated within the AMOS center and the Open AI Lab at NTNU, which offer unique expertize in the field of safety for autonomous systems and AI. The companies DNVGL and Kongsberg Maritime will be fully active project partners.
Project leader: Sebastien Gros
Institution: Institutt for teknisk kybernetikk