Josiah Hanna


Planning under uncertainty is a central problem in developing intelligent autonomous systems. The traditional representation for these problems is a Markov Decision Process (MDP). The MDP model can be extended to a Multi-criteria MDP (MMDP) for planning under uncertainty while trying to optimize multiple criteria. However, due to the trade-offs involved in multi criteria problems there may be infinitely many optimal solutions. The focus of this project has been to find a method that efficiently computes a subset of solutions that represents the entire set of optimal solutions for bi-objective MDPs.