Jessie Yang and Cong Shi receive funding from AFOSR for human-autonomy teaming
U-M IOE professors receive AFOSR funding to further their research on human-autonomy teaming.
U-M IOE professors receive AFOSR funding to further their research on human-autonomy teaming.
Jessie Yang and Cong Shi, U-M Industrial and Operations Engineering assistant and associate professors, respectively, have received funding from the Air Force Office of Scientific Research (AFOSR). Yang and Shi will use the funding to continue their research on human-autonomy teaming. They are serving as the sole PI’s for the project.
“Human-autonomy teaming is a major emphasis in the ongoing transformation of Air Force operations,” said Yang. “As autonomous and robotic systems become more capable in perception, planning, learning and action, there is an increasing possibility that they will become fully-fledged team members to humans, not merely instruments.”
As autonomous and robotic systems make this transition, humans and autonomous agents will be expected to work as a team in environments subject to uncertainty and dynamic changes. This project aims to explore effective teaming strategies that the autonomous agents could use when collaborating with humans, as well as developing the algorithms which enable the autonomous agents to do so.
“Human-autonomy teaming is an important facet of the AI revolution and we are excited to contribute to this mission,” said Shi.
AFOSR serves as the management and leadership behind scientific research for the United States Air Force. Their primary goal as an organization is to be at the forefront of developing science and technology in order to better equip the Air Force.
Jessie Yang joined U-M IOE in 2016 after completing a postdoctoral fellowship at MIT. Beyond human-agent teaming, Yang’s main research interests include human factors in high-risk industries. She is also a core faculty member of the U-M IOE’s Center for Ergonomics and an affiliate faculty member of the U-M Robotics Institute.
Shi’s primary research interests are focused on the design of efficient algorithms with theoretical performance guarantees for stochastic optimization models in operations management.
Yang and Shi are also conducting other research related to human-autonomy teaming. Funded by the U.S. Army Combat Capabilities Development Command Army Research Laboratory (ARL) the research is focused on improving the trust dynamics between human agents and autonomous agents.