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dc.contributor.authorDemaine, Erik D
dc.date.accessioned2021-01-26T14:41:54Z
dc.date.available2021-01-26T14:41:54Z
dc.date.issued2019-11
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/1721.1/129562
dc.description.abstractSelf-assembly refers to the process by which small, simple components mix and combine to form complex structures using only local interactions. Designed as a hybrid between tile assembly models and cellular automata, the Tile Automata (TA) model was recently introduced as a platform to help study connections between various models of self-assembly. However, in this paper we present a result in which we use TA to simulate arbitrary systems within the amoebot model, a theoretical model of programmable matter in which the individual components are relatively simple state machines that are able to sense the states of their neighbors and to move via series of expansions and contractions. We show that for every amoebot system, there is a TA system capable of simulating the local information transmission built into amoebot particles, and that the TA “macrotiles†used to simulate its particles are capable of simulating movement (via attachment and detachment operations) while maintaining the necessary properties of amoebot particle systems. The TA systems are able to utilize only the local interactions of state changes and binding and unbinding along tile edges, but are able to fully simulate the dynamics of these programmable matter systems.en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-030-26807-7_8en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleSimulation of Programmable Matter Systems Using Active Tile-Based Self-Assemblyen_US
dc.typeArticleen_US
dc.identifier.citationAlumbaugh, John Calvin et al. “Simulation of Programmable Matter Systems Using Active Tile-Based Self-Assembly.” Lecture Notes in Computer Science, 11648 (November 2019): 140-158 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-09T16:10:17Z
dspace.orderedauthorsAlumbaugh, JC; Daymude, JJ; Demaine, ED; Patitz, MJ; Richa, AWen_US
dspace.date.submission2020-12-09T16:10:26Z
mit.journal.volume11648en_US
mit.licenseOPEN_ACCESS_POLICY


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