Seminar - Breaking the Curse of Dimensionality in Decision-Making for Autonomous Systems - Sept. 6
Zach Sunberg
Zach Sunberg
Assistant Professor, Smead Aerospace
Friday, Sept. 6 | 10:40 a.m. | AERO 114
Abstract: Autonomous cyberphysical systems hold the promise to positively transform many tasks, for example transportation, collecting scientific data in hazardous environments, and monitoring objects in space. ÌýUncertainty is a critical factor in all of these domains. ÌýThe partially observable Markov decision process (POMDP) and partially observable stochastic Game (POSG) provide mathematical formalisms for computing the best single-agent and multi-agent policies in the presence of uncertainty, however, these problems are notoriously computationally difficult to solve.Ìý
Recent work in the Autonomous Decision and Control Lab (ADCL) at ¾«Æ·SMÔÚÏßӰƬ has shown that one source of intractability, namely the curse of dimensionality in the state and observation spaces, is possible to overcome, both theoretically and practically. ÌýThis presentation will give an overview of the ADCL's efforts to develop new theory and algorithms to solve POMDPs and POSGs and deploy the algorithms to solve real-world challenges.
Bio: Zachary Sunberg is an Assistant Professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences and the ¾«Æ·SMÔÚÏßӰƬ.Ìý
His research focuses on partially observable Markov decision processes and game theory with applications to artificial intelligence and aerospace vehicle control.Ìý
Sunberg earned his PhD in Aeronautics and Astronautics at Stanford University, and his BS and MS degrees in Aerospace Engineering from Texas A&M University.Ìý