Guest Talk: Motion Planning under Uncertainty and Partial Observability
Thursday, 07.12.2017, 11:00am
Location: RWTH Aachen University, Department of Computer Science - Ahornstr. 55, building E3, room 9u10
The subject of this talk are motion planning problems where agents move inside environments that are subject to uncertainties and potentially not fully observable.
The goal is to compute a strategy or a set of strategies for an agent that is guaranteed to satisfy certain safety or performance specifications. Such problems are naturally modeled by Markov decision processes or partially observable Markov decision processes.
We discuss several technical approaches, ranging from the computation of permissive strategies that guarantee safe reinforcement learning in unknown environments, a game-based abstraction framework for partially observable Markov decision processes, as well as the utilization of parameter synthesis for Markov chains to compute randomized strategies for partially observable Markov decision processes.
We also consider preliminary work on actively including humans into verification and synthesis processes, and what challenges arise.