Artificial Neural Networks

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Artificial Neural Networks

• Michael Prestia
• COT 4810
• March 18, 2008

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The Human Brain

• Composed of neurons
• Neurons send signals to each other

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Neurons

• Neurons store and transmit information
• Neurons send messages to one another through a
  synapse

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Artificial Neural Networks
Anatomical Sketch
ANN representation

• ANNs simulate neurons to create an artificial
  brain
• The symbol for a synapse in an ANN is a
  connecting line

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Neural Networks

• The brain takes inputs (senses) and produces
  outputs (actions)
• ANNs act in the same fashion

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Example

• A driving simulator can take in sensors as input
• The output will represent which direction to move

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Composition of an ANN

• Connections between neurons are called weights
• Linearly separable problems do not require a
  hidden layer

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How Does an ANN Work?

• Activation

Neuron j activation
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Recurrent Connections

• Allow feedback
• Represents a type of memory

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Training an Neural Network

• Targets can be either known or unknown
• Different types of training
• Target known
• Hebbian Learning
• Perceptron Learning
• Backpropogation
• Target unknown
• Neuroevolution

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Hebbian and Perceptron Learning

• Hebbian Learning
• Works best when output is independent of input
• Simple Hebbian Rule wi(new) wi(old) xiy
• Perceptron Learning
• changes to match target
• wi(new) wi(old) atxi

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Backpropagation

• Designed for at least one hidden layer
• General idea
• Let activation propagate to outputs
• Calculate and assign error values
• Adjust weights
• Sigmoid activation function is common

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Backpropagation (cont.)

• 5 steps
• Calculate error at outputs
• Ek (tk ok) ok(1-ok)
• Adjust weights going into output layer
• Wjk L Ek oj
• Calculate error at hidden nodes
• Ej ok(1-ok)
• Adjust weights going into hidden layer
• Wij L Ej oi
• Repeat

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Disadvantage of Backprop

• Easy to get stuck in local optima

http//content.answers.com/main/content/wp/en/6/67
/Fitness-landscape-cartoon.png
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Applications of Backprop

• Diagnose medical conditions based on past
  examples
• Learn mouse gesture recognition
• Learn to control anything by example

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Neuroevolution

• Uses a genetic algorithm to evolve the weights in
  a neural network
• Genome is direct encoding of weights
• Weights are optimized for the given task

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Code Example
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Disadvantage of Neuroevolution

• Competing Conventions Problem

3! 6 different representations of the same
network
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Other Types of Neuroevolution

• Topology and Weight Evolving Artificial Neural
  Networks (TWEANNS)
• NeuroEvolution of Augmenting Topologies (NEAT)

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Applications of Neuroevolution

• Factory optimization
• Game playing (Go, Tic-tac-toe)
• Visual recognition
• Video Games

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References

• Dewdney, A.K. The New Turing Omnibus. New York
  Henry Hold and Company, 1993.
• Buckland, Mat. AI Techniques for Game
  Programming. Cincinnati Premier Press, 2002.
• Machine Learning I Michael Georgiopoulos
• AI for Game Programming Kenneth Stanley
• Forza Motorsport 2 information from Official Xbox
  Magazine
• Neuron image from http//img460.imageshack.us/img4
  60/8744/neuron6ri.gif
• Local optima image from http//content.answers.com
  /main/content/wp/en/6/67/Fitness-landscape-cartoon
  .png
• All other images copied with permission from
  http//www.cs.ucf.edu/kstanley/cap4932spring08dir
  /CAP4932_lecture9.ppt and http//www.cs.ucf.edu/k
  stanley/cap4932spring08dir/CAP4932_lecture12.ppt
• Forza Motorsport 2 video from http//media.xbox360
  .ign.com/media/743/743956/vids_1.html

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Homework Questions

• What types of problems do not require a hidden
  layer?
• What are two different methods for training
  neural networks and how do they differ?