1,084 research outputs found
Sensitivity to noise and ergodicity of an assembly line of cellular automata that classifies density
We investigate the sensitivity of the composite cellular automaton of H.
Fuk\'{s} [Phys. Rev. E 55, R2081 (1997)] to noise and assess the density
classification performance of the resulting probabilistic cellular automaton
(PCA) numerically. We conclude that the composite PCA performs the density
classification task reliably only up to very small levels of noise. In
particular, it cannot outperform the noisy Gacs-Kurdyumov-Levin automaton, an
imperfect classifier, for any level of noise. While the original composite CA
is nonergodic, analyses of relaxation times indicate that its noisy version is
an ergodic automaton, with the relaxation times decaying algebraically over an
extended range of parameters with an exponent very close (possibly equal) to
the mean-field value.Comment: Typeset in REVTeX 4.1, 5 pages, 5 figures, 2 tables, 1 appendix.
Version v2 corresponds to the published version of the manuscrip
Cellular automaton supercolliders
Gliders in one-dimensional cellular automata are compact groups of
non-quiescent and non-ether patterns (ether represents a periodic background)
translating along automaton lattice. They are cellular-automaton analogous of
localizations or quasi-local collective excitations travelling in a spatially
extended non-linear medium. They can be considered as binary strings or symbols
travelling along a one-dimensional ring, interacting with each other and
changing their states, or symbolic values, as a result of interactions. We
analyse what types of interaction occur between gliders travelling on a
cellular automaton `cyclotron' and build a catalog of the most common
reactions. We demonstrate that collisions between gliders emulate the basic
types of interaction that occur between localizations in non-linear media:
fusion, elastic collision, and soliton-like collision. Computational outcomes
of a swarm of gliders circling on a one-dimensional torus are analysed via
implementation of cyclic tag systems
Density Classification Quality of the Traffic-majority Rules
The density classification task is a famous problem in the theory of cellular
automata. It is unsolvable for deterministic automata, but recently solutions
for stochastic cellular automata have been found. One of them is a set of
stochastic transition rules depending on a parameter , the
traffic-majority rules.
Here I derive a simplified model for these cellular automata. It is valid for
a subset of the initial configurations and uses random walks and generating
functions. I compare its prediction with computer simulations and show that it
expresses recognition quality and time correctly for a large range of
values.Comment: 40 pages, 9 figures. Accepted by the Journal of Cellular Automata.
(Some typos corrected; the numbers for theorems, lemmas and definitions have
changed with respect to version 1.
Density classification on infinite lattices and trees
Consider an infinite graph with nodes initially labeled by independent
Bernoulli random variables of parameter p. We address the density
classification problem, that is, we want to design a (probabilistic or
deterministic) cellular automaton or a finite-range interacting particle system
that evolves on this graph and decides whether p is smaller or larger than 1/2.
Precisely, the trajectories should converge to the uniform configuration with
only 0's if p1/2. We present solutions to that problem
on the d-dimensional lattice, for any d>1, and on the regular infinite trees.
For Z, we propose some candidates that we back up with numerical simulations
Designing complex dynamics in cellular automata with memory
Since their inception at Macy conferences in later 1940s, complex systems have remained the most controversial topic of interdisciplinary sciences. The term "complex system" is the most vague and liberally used scientific term. Using elementary cellular automata (ECA), and exploiting the CA classification, we demonstrate elusiveness of "complexity" by shifting space-time dynamics of the automata from simple to complex by enriching cells with memory. This way, we can transform any ECA class to another ECA class - without changing skeleton of cell-state transition function - and vice versa by just selecting a right kind of memory. A systematic analysis displays that memory helps "discover" hidden information and behavior on trivial - uniform, periodic, and nontrivial - chaotic, complex - dynamical systems. © World Scientific Publishing Company
A guided tour of asynchronous cellular automata
Research on asynchronous cellular automata has received a great amount of
attention these last years and has turned to a thriving field. We survey the
recent research that has been carried out on this topic and present a wide
state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat
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