Abstract
In this talk, we present our algorithm for energy distribution in programmable matter that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms. As a supporting but independent result, we extend the amoebot model’s well-established spanning forest primitive so that it self-stabilizes in the presence of crash failures. We conclude by showing how this self-stabilizing primitive can be leveraged to compose our energy distribution algorithm with existing amoebot model algorithms, effectively generalizing previous work to also consider energy constraints.
Joshua J. Daymude
Assistant Professor, SCAI & CBSS
I am a Christian and assistant professor in computer science studying collective emergent behavior and programmable matter through the lens of distributed computing, stochastic processes, and bio-inspired algorithms. I also love gaming and playing music.
