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Artificial Life

Artificial Life. Using Recurrent Neural Networks and Hebbian Learning Michael Tauraso CS 152. Organisms. Behavior decided by a Recurrent Neural Network (RNN) RNN derived from a genome Organisms have a 360 degree visual system. Gene Encoding. Direct Encoding (boring)

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Artificial Life

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  1. Artificial Life Using Recurrent Neural Networks and Hebbian Learning Michael Tauraso CS 152

  2. Organisms • Behavior decided by a Recurrent Neural Network (RNN) • RNN derived from a genome • Organisms have a 360 degree visual system

  3. Gene Encoding • Direct Encoding (boring) • Graph Grammar (interesting) • Cellular Encoding (Interesting) • Functional Block Problem

  4. Graph Grammar • Goal is to generate an adjacency matrix • Each non-terminal generates a 2x2 matrix • Each 2x2 matrix contains either numbers or other non-terminals. • The start symbol is used to build up an adjacency matrix

  5. Cellular Encoding • Fundamental unit of genome is a transformation. • Transformations act on a particular neuron • A sequence of transformations defines a neural network. • This preserves functional blocks better

  6. Grid World • World is a wrap-around grid • Each grid can have 1 organism • Each grid can have food on it for the organisms to eat • Organisms have a 360 degree visual system • Organisms use an RNN to decide on their actions

  7. Learning Method • Recurrent networks • Hebbian Learning • Clamping Outputs

  8. Results? • Grid World difficult to implement • Two Interesting encodings remain unimplemented • Interesting extensions to the world model remain unimplemented

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