Abstract
Wolfram famously developed a four-way classification of CA behaviour, with Class IV containing CAs that generate complex, localised structures. However, finding Class IV rules is far from straightforward, and can require extensive, time-consuming searches. This work presents a Convolutional Neural Network (CNN) that was trained on visual examples of CA behaviour, and learned to classify CA images with a high degree of accuracy. I propose that a refinement of this system could serve as a useful aid to CA research, automatically identifying possible candidates for Class IV behaviour and universality, and significantly reducing the time required to find interesting CA rules.
Issue Section:
Complex Dynamical Systems
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© 2019 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2019
Massachusetts Institute of Technology
Issue Section:
Complex Dynamical Systems