Simplification Of Decision-Making Under Uncertainty

Simplification Of Decision-Making Under Uncertainty


Decision-making under uncertainty in partially observable domains is at the heart of any single- and multi-agent autonomous system acting with imperfect information in stochastic environments. In this project we develop novel approaches for simplification of these challenging problems, aiming to accelarate decision-making while providing formal performance guarantees. The project seeks to advance fundamental theory as well as develop prototype demonstrations on real robots.

Prerequisites:

  • Strong analytical skills, passion for rigorous mathematical formulations
  • Strong programming skills (preferably Python or Julia).
  • Background in probability and measure theory, MDPs/POMDPs, and (deep) reinforcement learning is an advantage.

Academic supervisor:

Duration: 1 or 2 semesters