Self-Organizing Particle Systems

Project: Stochastic SOPS

We utilize tools from statistical physics and Markov chain analysis to investigate how macro-scale behaviors in distributed systems of programmable matter can naturally emerge from local micro-behaviors by individual computational units. When this stochastic approach is combined with the amoebot model from the SOPS project, we translate Markov chains defined at a system level into asynchronous, distributed, local algorithms for self-organizing particle systems. These stochastic algorithms use very little memory and communication relative to the (mostly) deterministic SOPS algorithms, instead utilizing probability to drive emergent phenomenon.

Publications

Refereed Journal Papers

  • [Accepted] A stochastic approach to shortcut bridging in programmable matter. Marta Andrés Arroyo, Sarah Cannon, Joshua J. Daymude, Dana Randall, and Andréa W. Richa. Invited submission to the DNA Computing and Molecular Programming — 23rd International Conference (DNA23) Special Issue of the Journal of Natural Computing.

Refereed Conference Proceedings

Presentations

Invited Talks

Conference Talks

Poster Presentations

Other Presentations

People

Current Team

Andréa W. Richa

Andréa W. Richa

PI / Professor, Arizona State University

[Website]
Cem Gokmen

Cem Gokmen

Undergraduate Researcher, Georgia Institute of Technology

[Website]
Dana Randall

Dana Randall

PI / Professor, Georgia Institute of Technology

[Website]
Sarah Cannon

Sarah Cannon

PhD, Georgia Institute of Technology 2018

[Website]
Joshua J. Daymude

Joshua J. Daymude

PhD Student, Arizona State University

[Website]

Past Members

Marta Andrés Arroyo

Marta Andrés Arroyo

Undergraduate Researcher, University of Granada

[Website]

Funding