### 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 language | English (US) |
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Title of host publication | PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing |

Publisher | Association for Computing Machinery |

Pages | 483-485 |

Number of pages | 3 |

ISBN (Print) | 9781450357951 |

DOIs | |

State | Published - Jul 23 2018 |

Event | 37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018 - Egham, United Kingdom Duration: Jul 23 2018 → Jul 27 2018 |

### Other

Other | 37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018 |
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Country | United Kingdom |

City | Egham |

Period | 7/23/18 → 7/27/18 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Software
- Hardware and Architecture
- Computer Networks and Communications

### Cite this

*PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing*(pp. 483-485). Association for Computing Machinery. https://doi.org/10.1145/3212734.3212792

**Brief announcement : A local stochastic algorithm for separation in heterogeneous self-organizing particle systems.** / Cannon, Sarah; Daymude, Joshua J.; Gokmen, Cem; Randall, Dana; Richa, Andrea.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing.*Association for Computing Machinery, pp. 483-485, 37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018, Egham, United Kingdom, 7/23/18. https://doi.org/10.1145/3212734.3212792

}

TY - GEN

T1 - Brief announcement

T2 - A local stochastic algorithm for separation in heterogeneous self-organizing particle systems

AU - Cannon, Sarah

AU - Daymude, Joshua J.

AU - Gokmen, Cem

AU - Randall, Dana

AU - Richa, Andrea

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

SP - 483

EP - 485

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

PB - Association for Computing Machinery

ER -