HCC: Small: Leadership Emergence for Synchronous Human-Autonomy Teaming

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

Machines are increasingly autonomous, capable of performing many complex tasks with little or no supervision. However, they still often need to work on teams with humans in many real-world applications, where humans provide diverse, redundant, and complementary skills that make the systems more resilient. Thus, being able to build autonomous machines that can team well with humans will become increasingly important in emerging applications such as driverless cars and medical diagnosis. The research in this project moves toward allowing machines to collaborate as humans do in large organizations, especially in terms of leading, following, and synchronizing with others to complete critical tasks in both routine and emergency situations. Ultimately, the project will enhance the abilities of intelligent machines to work with humans to improve business operations and serve the public in a robust and reliable manner. The project seeks to define the emergent nature of leadership and cognitive synchronization for integrating autonomy into multi-agent organizations. The project team will ground the work in implicit leadership theory, which predicts that leading and following dynamics in human teams tend to result in granting leadership to the most competent and trustworthy member regardless of a pre-defined authority structure. To leverage this theory, the researchers will develop methods to support cognitive synchronization across diverse tasks according to skill-, rule-, and knowledge-based behaviors that classify human cognition and underlie assessments of trust. Reinforcement learning approaches will be developed for selecting interaction methods and improving interaction choices to promote long-term, productive collaboration. These methods will be evaluated in computer simulations and field experiments with unmanned vehicles and human teammates in the context of search and rescue missions. The design principles and algorithms will be applicable to many domains involving command and control with humans in the loop, such as disaster response, air traffic control, and patient flow management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.