Our research focuses on artificial intelligence for controlling complex systems. We usually focus on autonomous aerospace systems, from space probes to drones to telescopes for tracking space debris, but we have also worked on other systems including ecological communities and autonomous cars. The theme that unites all of our research is uncertainty. This could be uncertainty in the system’s parameters or states or in how other people, systems, or the environment will interact with it. We approach our research from many perspectives, ranging from pure mathematical theory to numerical simulation to hardware experiments. The mathematical formalisms that we most often use are the partially observable Markov decision process (POMDP) for stochastic uncertainty, or various game formalisms when the uncertainty is worst case or introduced by other rational agents. One of our most important specialties is developing online tree search algorithms for POMDPs, and we are one of the world’s leading centers of development for this approach.

Autonomous Decision and Control Lab

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Autonomous Decision and Control Lab

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