Abstract
The computational power of a computation model may be roughly defined by “what it is able to compute”. At this level, cellular automata have the same computational power as Turing machines, PRAM or boolean circuits for example. In order to get more subtle understanding and results, one has to compare their performances, on the computational functions or problems, according to criteria, which depend, more or less, on their own features or on features of some variants. Time and space are the natural and basic resources of all models, and they give rise to complexity classes, that may be refined according to some characteristics of the model. In the cellular automata case, the input-output location issue is worthy to take into account.
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Delorme, M., Mazoyer, J. (1999). Cellular Automata as Languages Recognizers. In: Delorme, M., Mazoyer, J. (eds) Cellular Automata. Mathematics and Its Applications, vol 460. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9153-9_5
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DOI: https://doi.org/10.1007/978-94-015-9153-9_5
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