Restricted Density Classification in One Dimension - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Restricted Density Classification in One Dimension

Siamak Taati
  • Function : Author
  • PersonId : 978944


The density classification task is to determine which of the symbols appearing in an array has the majority. A cellular automaton solving this task is required to converge to a uniform configuration with the majority symbol at each site. It is not known whether a one-dimensional cellular automaton with binary alphabet can classify all Bernoulli random configurations almost surely according to their densities. We show that any cellular automaton that washes out finite islands in linear time classifies all Bernoulli random configurations with parameters close to 0 or 1 almost surely correctly. The proof is a direct application of a “percolation” argument which goes back to Gács (1986).
Fichier principal
Vignette du fichier
338243_1_En_18_Chapter.pdf (297.58 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01442476 , version 1 (20-01-2017)





Siamak Taati. Restricted Density Classification in One Dimension. 21st Workshop on Cellular Automata and Discrete Complex Systems (AUTOMATA), Jun 2015, Turku, Finland. pp.238-250, ⟨10.1007/978-3-662-47221-7_18⟩. ⟨hal-01442476⟩
146 View
58 Download



Gmail Facebook X LinkedIn More