Abstract
We investigate the computational capabilities of probabilistic cellular automata by means of the density classification problem. We find that a specific probabilistic cellular automata rule is able to solve the density classification problem, i.e. classifies binary input strings according to the number of 1's and 0's in the string, and show that its computational abilities are related to critical behaviour at a phase transition. © 2009 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Schüle, M., Ott, T., & Stoop, R. (2009). Computing with probabilistic cellular automata. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5769 LNCS, pp. 525–533). https://doi.org/10.1007/978-3-642-04277-5_53
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