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CAP6938 Neuroevolution and Developmental Encoding Overview of NEAT and CPPNs

CAP6938 Neuroevolution and Developmental Encoding Overview of NEAT and CPPNs. Dr. Kenneth Stanley August 28, 2006. Why an Overview?. Preparation for declaring projects Get an idea what is possible Sufficient background without details Technical details coming later in semester.

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CAP6938 Neuroevolution and Developmental Encoding Overview of NEAT and CPPNs

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  1. CAP6938Neuroevolution and Developmental EncodingOverview of NEAT and CPPNs Dr. Kenneth Stanley August 28, 2006

  2. Why an Overview? • Preparation for declaring projects • Get an idea what is possible • Sufficient background without details • Technical details coming later in semester

  3. NEAT: NeuroEvoluton of Augmenting Topologies • Evolutionary algorithm applied to evolving artificial neural networks (ANNs) • Evolution (i.e. genetic algorithms) is a way to optimize ANN topology and weights • NEAT evolves increasingly complex ANNs • Able to solve difficult control and decision-making tasks

  4. Evolution:Survival of the Roundest Gen 1 Select as parents Gen 2 Select as parents Gen 3 Champ!

  5. NeuroEvolution • Evolve ANNs • Evaluated for performance on a task • Controlling a vehicle or robot • Drawing a picture • Recognizing an image • Warning about danger • Playing a board game • Playing a video game • Playing music • The fittest survive and reproduce

  6. ANNs can Sense and React

  7. NEAT Features • NEAT is unique: Networks complexify over evolution • So they can find the right complexity for the task • NEAT solves problems with systems of diverse networks using historical marking and speciation • A first step in evolving complex systems Crash Warning Champ

  8. NEAT Applications • NEAT makes new applications possible and has set performance records Double Pole Balancing Record Vehicle Warnings Keepaway Record NERO: Real-time Neuroevolution in a Video Game Robot Duel Go Hopper

  9. Not Just About Control and Classification Interactive Picture Evolution Interactive Chord Progression Evolution Interactive Drum Pattern Evolution Guitar Effect-Pedal Emulation Interactive Particle Effect Evolution

  10. Non-neural NEAT • ANNs aren’t the only thing that can complexify • Anything with arbitrary structure can potentially be evolved • Cellular Automata • Lego Structures • FSMs • L-systems L-System Plant Geometry Generator by Hung-Wen Chen

  11. What Can’t NEAT Do? • “Regular” NEAT is a direct encoding • It doesn’t encode regularities or symmetries • Regularity and symmetry are believed to be essential to representing and searching complex structures: gene reuse • Giant complex structures in nature are composed of patterns • Patterns have regularities • Indirect encodings capture such information efficiently

  12. Indirect Encoding • Most are developmental encodings • Based on embryo to adult development • “Artificial Embryogeny” • This is how encoding works in nature • Many have been proposed • None close to natural complexity

  13. Proposed Indirect Encodings

  14. What’s missing? • None implement complexification as in NEAT: maybe genes can be added and marked like in NEAT • For example: L-system NEAT? • Something’s still missing…

  15. A Recent Discovery • Drawn by a NEAT-evolved neural network!?

  16. Compositional Pattern Producing Networks (CPPNs) • Networks of functions composed to produce patterns with regularities • An abstraction of natural development without developmental simulation • Complexifying repeating patterns, regularities, symmetries, repetition with variation, etc.

  17. CPPN-NEAT • A new kind of indirect encoding • Elegantly captures regularities • Convert patterns into phenotypes • An entirely new research area Exploiting Regularity Without Development by Kenneth O. Stanley, To appear in: Proceedings of the AAAI Fall Symposium on Developmental Systems. Meno Park, CA: AAAI Press, 2006 (8 pages)

  18. Music is a Pattern over Time • A CPPN is a pattern generator that can produce repetition with variation, just like music • A CPPN can produce several related patterns from an underlying simpler pattern (multiple instruments) • Music generation is a promising demonstration

  19. Homework for 8/30/06 • Neural network weight-learning algorithms • Remember proposals/partners due 9/11 Fausett pp. 39-80 (in Chapter 2)Fausett pp. 289-316 (in Chapter 6)Chapter 1 of Sutton (on RL): Online Book ch.1Optional: Kaelbling RL survey paper

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