EAGER:AI-DCL:Cognitive-Behavior Model to Predict Human Reaction to Swarm AI Non-Compiance

This project studies autonomous swarm systems, which consist of a large number of collaborating robots that work together with a few human supervisors. Such systems promise unmatched task parallelism, fault-tolerance, resilience, and keeping humans out of harm's way. However, there are important technical issues that stand in the way of realizing these promises. Specifically, there is a sizeable lack of understanding of how human supervisors (who provide tactical input and mission intent, as opposed to direct or indirect control) interact with embodied swarm intelligence. That understanding is imperative for identifying solutions to mitigating cognitive overload and preserving human-swarm trust and shared situation awareness, especially in humans-swarm teams that are actually scalable to practical applications. The researchers will leverage their engagement with particular stakeholder groups including the Buffalo Fire Department and Government/Industrial parties involved in Oil Spill Response. The project will also have impact on education, training, and broader research community through the development of curricular materials for AI and Robotics courses, multi-robotic workshops at pertinent conferences, and public dissemination of data and models. The researchers will conduct a set of experiments and identify modeling approaches to answer fundamental questions regarding how humans respond to the behavior of embodied swarm agents. Those questions include how humans react to circumstantial or deliberate non-compliance by the swarm and the level of feedback provided by the swarm to explain any non-compliance, how humans identify and attribute errors (or perceived errors) to swarms, and how this impacts their intervention frequency. They will use a neuro-ergonomic approach to estimate human metrics such as event-related (brain) potential, intervention tendencies, situation awareness, and cognitive workload via physiological information. On the machine intelligence side, they will design decentralized swarm behavior to allow studying impact of unique factors such as non-compliance and feedback levels in search and object transport applications. The researchers have complementary expertise in the areas of human-robot interaction, human psycho-physiological monitoring, and autonomous swarm systems for engaging in the synergistic research activities that are central to the project. They also have access to key research facilities including a large motion capture environment, a swarmbot arena, aerial/ground swarm robotic platforms, brain-computer interfaces, and physiological monitoring setups. 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.