Abstract
This paper details a method for optimising the size of Boolean automata networks in order to compute their attractors under the parallel update schedule. This method relies on the formalism of modules introduced recently that allows for (de)composing such networks. We discuss the practicality of this method by exploring examples. We also propose results that nail the complexity of most parts of the process, while the complexity of one part of the problem is left open.
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Acknowledgements
The works of Kévin Perrot and Sylvain Sené were funded mainly by their salaries as French State agents, affiliated to Aix-Marseille Univ., Univ. de Toulon, CNRS, LIS, UMR 7020, Marseille, France (both) and to Univ. Côte d’Azur, CNRS, I3S, UMR 7271, Sophia Antipolis, France (KP), and secondarily by ANR-18-CE40-0002 FANs project, ECOS-Sud C19E02 project, STIC AmSud CoDANet 19-STIC-03 (Campus France 43478PD) project.
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Perrot, K., Perrotin, P., Sené, S. (2021). Optimising Attractor Computation in Boolean Automata Networks. In: Leporati, A., Martín-Vide, C., Shapira, D., Zandron, C. (eds) Language and Automata Theory and Applications. LATA 2021. Lecture Notes in Computer Science(), vol 12638. Springer, Cham. https://doi.org/10.1007/978-3-030-68195-1_6
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