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 -