Birds flock in order to forage and move more efficiently. Fish school to avoid predators. And bees swarm to reproduce. Recent advances in artificial intelligence have sought to mimic these natural behaviors as a way to potentially improve search-and-rescue operations or to identify areas of wildfire spread over vast areas—largely through coordinated drone or robotic movements. However, developing a means to control and utilize this type of AI—or “swarm intelligence”—has proved challenging. In a newly published paper, an international team of scientists describes a framework designed to advance swarm intelligence—by controlling flocking and swarming in ways that are akin to what occurs in nature.

The paper proposes a generic risk theory that treats risk as information produced by human cognition. It introduces a quantitative descriptive model linking spontaneous risk perception and analytical risk cognition through disparities between target and realistic value expectations, outlines conditions for when perception occurs, and connects the framework to decision-making and potential AI-enabled implementations.

WASHINGTON, D.C. — A U.S. Naval Research Laboratory (NRL) research team successfully conducted the first reinforcement learning (RL) control of a free-flyer in space on May 27.