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convoca

Demonstrate and learn cellular automata using convolutional neural networks in TensorFlow

This code is associated with the ArXiv preprint: Gilpin, William. "Cellular automata as convolutional neural networks" 2018. https://arxiv.org/abs/1809.02942

For now, code is only in archival form for testing and analysis; future versions of this repository will significantly re-factor code into a general-purpose tool for cellular automaton analysis. All versions until a 1.0/PyPI release are thus tentative.

Installation and Requirements

Typical installation time should be 5-10 minutes using Miniconda. This code should work on any operating system supported by Anaconda, but it has only been tested on OSX and Ubuntu.

  • Python >3.4
  • TensorFlow
  • numpy
  • matplotlib
  • Jupyter notebooks (for demos)

Structure

The package contains the following libraries

train_ca : requires TensorFlow

ca_funcs : requires TensorFlow

utils : minor functions that support the main methods. Requires numpy only.

Demos

The demos illustrates a minimal example of training a CNN on the Game of Life, including example outputs.

To Do

  • Add methods for simulating totalistic CA
  • Add methods for Moore neighborhood CA
  • Add demos recreating classic experiments, such as the results in Langton. Physica D, 1990.
  • Add statistical physics calculations such as an efficient calculation of "activity" for a CA
  • CA on graphs using an adjacency matrix --> grid convolutional operator