A local behavior identification algorithm for generative network automata configurations

Abstract

Relation between the part and the whole is investigated in the context of complex discrete dynamical systems. For that purpose, an algorithm for local behavior identification from global data described as Generative Network Automata model configurations is developed. It is shown that one can devise a procedure to simulate finite GNA configurations via Automata Networks having static rule-space setting. In practice, the algorithm provides an automated approach to model construction and it can suitably be used in GNA based system modeling effort. © 2011 Springer-Verlag.