SE367 Project Final Presentation By: Sujith Thomas Parimi Krishna Chaitanya In charge:- Prof Amitabha Mukerjee

 To make a neural net learn the rules of Conway’s game of life and predict the next generation of cells.  To identify oscillators and other emergent patterns using recurrent neural networks.

 Simple rules of Conway’s game of life  Emergence of complex patterns  Backpropagated neural network  Recurrent neural networks

 Training Neural Network to learn the rules of Conway’s game of life  Training a Recurrent Neural Network to detect a repeated pattern.

1.Input vector of size 9 2.Hidden layer has 9 nodes 3.Output layer has 1 node 4.We use bias at input and hidden layer 5.Our activation function is sigmoid 6.We update the weights through the backpropagation algorithm Features of training model

 Input vector of size 18  Hidden layer has 18 nodes  Output layer has 2 nodes  Bias is present at each layer  Activation function is Sigmoid  We are again updating weights through backpropagation.  In input vector the last 9 dimensions correspond to previous delayed state as shown.  We are using an array to store the previous 12 output states (size may vary later).

 The game has cells of 12 rows and 12 columns.  We use a seed of size 3X3 and4X4 to initialize the game.  We use a activation feedback from the output layer with a delay of 12 ticks.  This helps us to detect oscillators with period 1,2,3,4,6.

 Till now we have detected still lives and oscillators.  Till final demonstration we will show Gliders after they are recognized. The problem with gliders comes with their property of “Translation”  For solving this we can either use a 4 layer Neural Network or we have a heuristic of re-seeding.

OSCILLATORS

Still Lives

 A guide to Recurrent Neural Networks and Backpropagation, Mikael Boden, Halmstad University  Pattern Classification – Duda, Hart and Stork  Wikipedia – Conway’s Game of Life  Implementation of Neural Networks in C - John Bullinaria, University of Birmingham.