Abstract
It is considered the prospects of deep learning applications for identification problems for cellular automata (CA): classical CA, CA with memory and with strong anticipatory property are examined. Some identification problems for neighbors, rules, coefficients of CA are discussed. One simplest example of deep learning for classical CA is described. Some possibilities of deep learning application for identification problems for CA with memory and anticipation are proposed. The case of of deep learning for the systems with multivalued behavior had been proposed.
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References
Adamatsky, A.: Identification of cellular automata rules (1994)
Aggarwal, C.C.: Neural Networks and Deep Learning. A Textbook (2018). https://doi.org/10.1007/978-3-319-94463-0
Bandini, S., Mauri, G.: Multilayered cellular automata. Theor. Comput. Sci. 217(1), 99–113 (1999). https://doi.org/10.1016/S0304-3975(98)00152-2
Bolt, W., Baetens, J., De Baets, B.: Idenmtifying CAs with evolution algorithms. In: Proceedings of the International Conference on AUTOMATA-2013. Giessen Univ Press, pp. 11–20 (2013)
Krushinsky, D., Makarenko, A: Cellular automata with anticipation. examples and presumable applications. In: Dubois, D.M. (ed.) AIP Conference Proceedings, USA, vol. 1303, pp. 246–254 (2010)
Dubois, D.M.: Generation of fractals from incursive automata, digital diffusion and wave equation systems. BioSystems 43(2), 97–114 (1997). https://doi.org/10.1016/S0303-2647(97)01692-4
Dubois, D.M.: Computing anticipatory systems with incursion and hyperincursion. Int. J. Comput. Anticip. Syst. 2, 3–14 (1998). https://doi.org/10.1063/1.56331
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Ilachinski, A.: Cellular Automata A discrete Universe. World Scientific, Singapore (2001)
Kari, J.: Theory of cellular automata: a survey. Theor. Comput. Sci. 334(1–3), 3–33 (2005). https://doi.org/10.1016/j.tcs.2004.11.021
Makarenko, A.: Multivaluedness aspects in self-organization, complexity and computatios investigations by strong anticipation. In: Stoop, R., Chedjou, J., Li, Z., Kyamakya, K., Mathis, W. (eds.) Recent Advances in Nonlinear Dynamics and Sinchronization, pp. 33–54. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58996-1_3
Martinez, G.J., Adamatzky, A., Alonso-Sanz, A.: Complex dynamics of elementary cellular automata emerging from chaotic rules. Int. J. Bifurc. Chaos 1274(2) (2010). https://doi.org/10.1142/S021812741250023X
Rosen, R.: Anticipatory Systems. Philosophical, Mathematical, and Methodological Foundations, vol. 39. Springer, New York (2008)
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., Van Den Driessche, G., Graepel, T., Hassabis, D.: Mastering the game of Go without human knowledge. Nature 550(7676), 354–359 (2017). https://doi.org/10.1038/nature2427
Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002). http://www.wolframscience.com
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Popov, A., Makarenko, A. (2020). Presumable Applications of Deep Learning for Cellular Automata Identification. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-16841-4_13
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DOI: https://doi.org/10.1007/978-3-030-16841-4_13
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