In a swarm, each individual agent follows relatively simple rules that are not coordinated by a global aggregate objective. Nevertheless, observance of simple rules by the individual agents may lead to unexpected and complex behaviors that do achieve a macroscopic objective. In this presentation, we shall explore how when relatively simple rules are followed by individual agents that this can lead to interesting emerging behaviors in the aggregate. Given a set of rules, can we predict the emerging behavior? Given an emerging behavior, can we reverse-engineer the rules?
Dr. Matthew Alan Beauregard is an associate professor of Mathematics at Stephen F. Austin State University. He is an adjunct professor at the Center of Astrophysics, Space, and Engineering housed at the Baylor Research Innovative Collaborative. He has served as a faculty member at Clarkson University and was a post-doctoral researcher at Baylor University and the University of Arizona. In the past decade he has mentored over 24 undergraduate students in undergraduate interdisciplinary research activities in robotic, civil, and mechanical engineering, computational science, statistics, and applied mathematics. He has authored over 20 peer-reviewed publications; 8 of which were with students. He earned his doctoral degree in applied mathematics with a minor in aerospace and mechanical engineering from the University of Arizona and undergraduate degree in mathematics from the University of New Hampshire.