2026-07-23T04:33:05Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:17242 2024-03-06T10:59:18Z ddc:004 open_access

Brief Announcement: Foraging in Particle Systems via Self-Induced Phase Changes Oh, Shunhao Randall, Dana Richa, Andréa W. Foraging self-organized particle systems compression phase changes The foraging problem asks how a collective of particles with limited computational, communication and movement capabilities can autonomously compress around a food source and disperse when the food is depleted or shifted, which may occur at arbitrary times. We would like the particles to iteratively self-organize, using only local interactions, to correctly gather whenever a food particle remains in a position long enough and search if no food particle has existed recently. Unlike previous approaches, these search and gather phases should be self-induced so as to be indefinitely repeatable as the food evolves, with microscopic changes to the food triggering macroscopic, system-wide phase transitions. We present a stochastic foraging algorithm based on a phase change in the fixed magnetization Ising model from statistical physics: Our algorithm is the first to leverage self-induced phase changes as an algorithmic tool. A key component of our algorithm is a careful token passing mechanism ensuring a dispersion broadcast wave will always outpace a compression wave. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Shunhao Oh and Dana Randall and Andréa W. Richa 2022 Is Part Of LIPIcs, Volume 246, 36th International Symposium on Distributed Computing (DISC 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.DISC.2022.51 urn:nbn:de:0030-drops-172423 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2022.51 eng https://creativecommons.org/licenses/by/4.0/legalcode