Brief announcement: A local stochastic algorithm for separation in heterogeneous self-organizing particle systems

Sarah Cannon, Joshua J. Daymude, Cem Gokmen, Dana Randall, Andrea Richa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationPODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing
PublisherAssociation for Computing Machinery
Pages483-485
Number of pages3
ISBN (Print)9781450357951
DOIs
StatePublished - Jul 23 2018
Event37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018 - Egham, United Kingdom
Duration: Jul 23 2018Jul 27 2018

Publication series

NameProceedings of the Annual ACM Symposium on Principles of Distributed Computing

Other

Other37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018
CountryUnited Kingdom
CityEgham
Period7/23/187/27/18

Keywords

  • Distributed algorithms
  • Markov chains
  • Programmable matter
  • Self-organization
  • Separation
  • Stochastic algorithms

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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    Cannon, S., Daymude, J. J., Gokmen, C., Randall, D., & Richa, A. (2018). Brief announcement: A local stochastic algorithm for separation in heterogeneous self-organizing particle systems. In PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing (pp. 483-485). (Proceedings of the Annual ACM Symposium on Principles of Distributed Computing). Association for Computing Machinery. https://doi.org/10.1145/3212734.3212792