Transcript LEARNING SEQUENCES FROM CONWAY’S GAME OF LIFE SE367 Project Final Presentation By:

SE367 Project Final Presentation
LEARNING SEQUENCES FROM CONWAY’S GAME
OF LIFE
By:
Sujith Thomas
Parimi Krishna Chaitanya
In charge:- Prof Amitabha Mukerjee
OBJECTIVE OF THE PROJECT
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To make a neural net learn the rules of Conway’s game of life
and predict the next generation of cells.
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To identify oscillators and other emergent patterns using
recurrent neural networks.
QUICK RECAP
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Simple rules of Conway’s game of life
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Emergence of complex patterns
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Backpropagated neural network
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Recurrent neural networks
STAGES OF THE PROJECT
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Training Neural Network to learn the rules of
Conway’s game of life
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Training a Recurrent Neural Network to detect a
repeated pattern.
TRAINING NEURAL NET TO LEARN RULES OF CONWAY’S GAME OF LIFE
Features of training model
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Input vector of size 9
Hidden layer has 9 nodes
Output layer has 1 node
We use bias at input and hidden
layer
5. Our activation function is sigmoid
6. We update the weights through the
backpropagation algorithm
TRAINING NEURAL NET TO LEARN RULES OF CONWAY’S GAME OF LIFE
TRAINING A RECURRENT NEURAL NET FOR RECOGNIZING REPEATED PATTERNS
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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).
TRAINING A RECURRENT NEURAL NET TO RECOGNIZE REPEATED PATTERNS
DETECTING EMERGENT PATTERNS
 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.
COMING TO RESULTS
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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.
SOME OF OUR OUTPUTS
OSCILLATORS
SOME OF OUR OUTPUTS
Still Lives
REFERENCES:
A guide to Recurrent Neural Networks and
Backpropagation, Mikael Boden, Halmstad
University 2001.
 Pattern Classification – Duda, Hart and Stork
 Wikipedia – Conway’s Game of Life
 Implementation of Neural Networks in C - John
Bullinaria, University of Birmingham.
http://www.cs.bham.ac.uk/~jxb/NN/nn.html
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