Bio-Inspired Energy Distribution for Programmable Matter

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.

Date
January 7, 2021 9:35 AM
Location
Nara Kasugano International Forum (Virtual Event)
Nara, Japan
Joshua J. Daymude
Joshua J. Daymude
Assistant Professor, Computer Science

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.

Jamison W. Weber
Jamison W. Weber
PhD Student, Computer Science

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