Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies

Hiroki Sayama

doi:10.1109/ALIFE.2007.367799

Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies

Hiroki Sayama^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Citations (Scopus)

Abstract

We propose a new modeling framework "Generative Network Automata (GNA)" that can uniformly describe both state transitions and autonomous topology transformations of complex dynamical networks. GNA is formulated as an extension of existing complex dynamical network models to include a new set of generative update rules that determine how local network topologies will change based on the current local network states and topologies. This paper introduces basic concepts of GNA, its formal definitions, its generality to represent other dynamical systems models, and some preliminary results of an exhaustive sweep of possible dynamics found in elementary binary GNA with restricted updating rules.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007
Pages	214-221
Number of pages	8
DOIs	https://doi.org/10.1109/ALIFE.2007.367799
Publication status	Published - 2007 Sept 25
Externally published	Yes
Event	1st IEEE Symposium on Artificial Life, IEEE-ALife'07 - Honolulu, HI, United States Duration: 2007 Apr 1 → 2007 Apr 5

Publication series

Name	Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007

Other

Other	1st IEEE Symposium on Artificial Life, IEEE-ALife'07
Country/Territory	United States
City	Honolulu, HI
Period	07/4/1 → 07/4/5

ASJC Scopus subject areas

Computational Theory and Mathematics
Biochemistry, Genetics and Molecular Biology(all)

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10.1109/ALIFE.2007.367799

Cite this

Sayama, H. (2007). Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies. In Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007 (pp. 214-221). Article 4218889 (Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007). https://doi.org/10.1109/ALIFE.2007.367799

Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies. / Sayama, Hiroki.
Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007. 2007. p. 214-221 4218889 (Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sayama, H 2007, Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies. in Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007., 4218889, Proceedings of the 2007 IEEE Symposium on Artificial Life, CI-ALife 2007, pp. 214-221, 1st IEEE Symposium on Artificial Life, IEEE-ALife'07, Honolulu, HI, United States, 07/4/1. https://doi.org/10.1109/ALIFE.2007.367799

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abstract = "We propose a new modeling framework {"}Generative Network Automata (GNA){"} that can uniformly describe both state transitions and autonomous topology transformations of complex dynamical networks. GNA is formulated as an extension of existing complex dynamical network models to include a new set of generative update rules that determine how local network topologies will change based on the current local network states and topologies. This paper introduces basic concepts of GNA, its formal definitions, its generality to represent other dynamical systems models, and some preliminary results of an exhaustive sweep of possible dynamics found in elementary binary GNA with restricted updating rules.",

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Generative network automata: A generalized framework for modeling complex dynamical systems with autonomously varying topologies

Abstract

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ASJC Scopus subject areas

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