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Introduction to CAP6938 Neuroevolution and Developmental Encoding

This class, led by Dr. Kenneth Stanley, explores the automated search for complexity in AI through neuroevolution and developmental encoding. We will discuss how simple systems evolve into complex structures, and how neural networks can efficiently represent these complexities. With a focus on achieving human-level intelligence, this course will examine the essential connections within the brain and the search methods researchers employ. Previous innovative projects are highlighted, showcasing the application of these concepts in AI development and evolution.

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Introduction to CAP6938 Neuroevolution and Developmental Encoding

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  1. Introduction to CAP6938Neuroevolution and Developmental Encoding Dr. Kenneth Stanley August 21, 2006

  2. What Is this Class About? • The automated search for complexity • Complexification: How simple systems increase in complexity • Developmental Encoding: How complex systems can be represented efficiently • Focus on neural networks, but not exclusively • A path to solving AI

  3. How Does this Relate to AI? • Human-level intelligence requires astronomical complexity • 100 trillion connections in the human brain • Researchers are looking for the right parts and how to combine them • We can do it by analysis or through search • This class is about automating the search through evolution

  4. Location within Science Neuroevolution: Evolving Neural Networks Biology AI EC Neuroscience NNs Developmental Encoding Consciousness?

  5. Some Prior Class Projects • Guitar Distortion Effect Inducer • Alife World • Optimal Distributed Sensor Placement • Riot Simulation • Interactive Particle Effect Evolution • Interactive Drum Rhythm Evolution • Chord Progression Evolution • Space Video Game • Simulated 2D Robot Body Evolution (Sodarace)

  6. Projects May Be Significant • Two invention disclosures • Several continued Ph.D. research topics

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