Fang will use her award to build on research that integrates game theory with machine learning to optimize communication and coordination to tackle real-world problems. Specifically, the NSF-funded research will examine scenarios where multiple agents with their own goals and preferences work together, like the collaboration between law enforcement agencies and local communities to protect wildlife from poachers.
"I hope that we can get a better understanding of the fundamental limits of communication and coordination in these multiagent scenarios," Fang said. "I also want to develop scalable and practical algorithms to help the agents reach those limits."
Fang will continue her work to stop poaching, this time examining how to incorporate into the scenario informants who might provide useful or misleading information to rangers in order to benefit the rangers or poachers. Fang will also look at ways to optimize communication from a platform to its users. One example of that work is how 412 Food Rescue, which seeks to reduce food waste by connecting extra food from stores and restaurants to places in need, can better notify and mobilize its volunteers to shuttle that food from one place to another.
Finally, Fang intends to look at systems that use correlated equilibrium as a communication model, where a mediator communicates privately with multiple agents to produce the best outcome. This could be a situation where an automated traffic monitor provides directions to autonomous vehicles approaching an intersection from different directions to ensure they pass safely and efficiently.
"I imagine this will become important in the future," Fang said.
Ding Zhao, an assistant professor in the Mechanical Engineering Department with a courtesy appointment in the Robotics Institute, also received an NSF Career Award to better understand the safety of artificial intelligence autonomy systems and design new theories and tools to develop better evaluation procedures.