TY - GEN

T1 - Brief announcement

T2 - 37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018

AU - Cannon, Sarah

AU - Daymude, Joshua J.

AU - Gokmen, Cem

AU - Randall, Dana

AU - Richa, Andrea

N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).

PY - 2018/7/23

Y1 - 2018/7/23

N2 - We investigate stochastic, distributed algorithms that can accomplish separation and integration behaviors in self-organizing particle systems, an abstraction of programmable matter. These particle systems are composed of individual computational units known as particles that have limited memory, strictly local communication abilities, and modest computational power, and which collectively solve system-wide problems of movement and coordination. In this work, we extend the usual notion of a particle system to treat heterogeneous systems by considering particles of different colors. We present a fully distributed, asynchronous, stochastic algorithm for separation, where the particle system self-organizes into segregated color classes using only local information about each particle's preference for being near others of the same color. Conversely, by simply changing the particles' preferences, the color classes become well-integrated. We rigorously analyze the convergence of our distributed, stochastic algorithm and prove that under certain conditions separation occurs. We also present simulations demonstrating our algorithm achieves both separation and integration.

AB - We investigate stochastic, distributed algorithms that can accomplish separation and integration behaviors in self-organizing particle systems, an abstraction of programmable matter. These particle systems are composed of individual computational units known as particles that have limited memory, strictly local communication abilities, and modest computational power, and which collectively solve system-wide problems of movement and coordination. In this work, we extend the usual notion of a particle system to treat heterogeneous systems by considering particles of different colors. We present a fully distributed, asynchronous, stochastic algorithm for separation, where the particle system self-organizes into segregated color classes using only local information about each particle's preference for being near others of the same color. Conversely, by simply changing the particles' preferences, the color classes become well-integrated. We rigorously analyze the convergence of our distributed, stochastic algorithm and prove that under certain conditions separation occurs. We also present simulations demonstrating our algorithm achieves both separation and integration.

KW - Distributed algorithms

KW - Markov chains

KW - Programmable matter

KW - Self-organization

KW - Separation

KW - Stochastic algorithms

UR - http://www.scopus.com/inward/record.url?scp=85052448245&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052448245&partnerID=8YFLogxK

U2 - 10.1145/3212734.3212792

DO - 10.1145/3212734.3212792

M3 - Conference contribution

AN - SCOPUS:85052448245

SN - 9781450357951

T3 - Proceedings of the Annual ACM Symposium on Principles of Distributed Computing

SP - 483

EP - 485

BT - PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing

PB - Association for Computing Machinery

Y2 - 23 July 2018 through 27 July 2018

ER -