1. CAP6938Neuroevolution and Developmental EncodingBasic Concepts Dr. Kenneth Stanley August 23, 2006

2. We Care About Evolving ComplexitySo Why Neural Networks? • Historical origin of ideas in evolving complexity • Representative of a broad class of structures • Illustrative of general challenges • Clear beneficiary of high complexity

3. How Do NNs Work? Output Output Input Input

4. How do NNs Work?Example Outputs (effectors/controls) Forward Left Right Front Left Right Back Inputs (Sensors)

5. What Exactly Happens Inside the Network? • Network Activation Neuron j activation: out1 out2 H1 H2 w11 w22 w21 w12 X1 X2

6. Recurrent connection out Wout-H wH-out H w11 w21 X1 X2 Recurrent Connections • Recurrent connections are backward connections in the network • They allow feedback • Recurrence is a type of memory

7. Activating Networks of Arbitrary Topology • Standard method makes no distinction between feedforward and recurrent connections: • The network is then usually activated once per time tick • The number of activations per tick can be thought of as the speed of thought • Thinking fast is expensive out Wout-H wH-out H w11 w21 X1 X2

8. Arbitrary Topology Activation Controversy • The standard method is not necessarily the best • It allows “delay-line” memory and a very simple activation algorithm with no special case for recurrence • However, “all-at-once” activation utilizes the entire net in each tick with no extra cost • This issue is unsettled

9. The Big Questions • What is the topology that works? • What are the weights that work? ? ? ? ? ? ? ? ? ? ? ? ? ?

10. Problem Dimensionality • Each connection (weight) in the network is a dimension in a search space • The space you’re in matters: Optimization is not the only issue! • Topology defines the space 21-dimensional space 3-dimensional space

11. High Dimensional Space is Hard to Search • 3 dimensional – easy • 100 dimensional – need a good optimization method • 10,000 dimensional – very hard • 1,000,000 dimensional – very very hard • 100,000,000,000,000 dim. – forget it

12. Bad News • Most interesting solutions are high-D: • Robotic Maid • World Champion Go Player • Autonomous Automobile • Human-level AI • Great Composer • We need to get into high-D space

13. A Solution (preview) • Complexification: Instead of searching directly in the space of the solution, start in a smaller, related space, and build up to the solution • Complexification is inherent in vast examples of social and biological progress

14. So how do computers optimize those weights anyway? • Depends on the type of problem • Supervised: Learn from input/output examples • Reinforcement Learning: Sparse feedback • Self-Organization: No teacher • In general, the more feedback you get, the easier the learning problem • Humans learn language without supervision

15. Significant Weight Optimization Techniques • Backpropagation: Change weights based on their contribution to error • Hebbian learning: Changes weights based on firing correlations between connected neurons Homework: -Fausett pp. 39-80 (in Chapter 2)-and Fausett pp. 289-316 (in Chapter 6) -Online intro chaper on RL -Optional RL survery