Zachary Sunberg's research focuses on decision making under uncertainty to enable safe and efficient autonomous vehicle operation. Specifically, his primary current focus is on problems where uncertainty is the most difficult aspect of the problem, primarily focusing on the partially observable Markov decision process (POMDP) and partially observable stochastic game (POSG) formalisms. His most significant contributions are in the area of online POMDP planning.
keywords
markov decision processes, partially observable markov decision processes, MDP, POMDP, human robot interaction, self-driving cars, urban air mobility, advanced air mobility, unmanned aerial vehicles, imperfect information games
Bayesian Optimized Monte Carlo Planning.
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
11880-11887.
2021
