Generalization over different cellular automata rules learned by a deep feed-forward neural network

• URL: http://arxiv.org/abs/2103.14886v1
• Date: Sat, 27 Mar 2021 12:12:07 GMT
• Title: Generalization over different cellular automata rules learned by a deep feed-forward neural network
• Authors: Marcel Aach, Jens Henrik Goebbert, Jenia Jitsev
• Abstract summary: A deep convolutional encoder-decoder network with short and long range skip connections is trained on various generated trajectories to predict the next CA state. Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To test generalization ability of a class of deep neural networks, we randomly generate a large number of different rule sets for 2-D cellular automata (CA), based on John Conway's Game of Life. Using these rules, we compute several trajectories for each CA instance. A deep convolutional encoder-decoder network with short and long range skip connections is trained on various generated CA trajectories to predict the next CA state given its previous states. Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations. To some extent, the network shows generalization to rule sets and neighborhood sizes that were not seen during the training at all.

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