1. Evolving Motor Techniques for Artificial Life Kelley Hecker, Period 7

2. Abstract  Creatures' fitness values improve from generation to generation  Co-evolution  Both the body and the brain develop  Possibility of specialized creatures

3. Methodology: Genome  Genome represented by a one- dimensional array  Each array has several nodes  Nodes represent body segments  Each node contains dimensions for body part, location of parent and child connections, and neuron functions  Genome converted to physical form for simulation

4. Example Genomes  Root Node:  Length: 3, Width: 2, Height: 2  Child 0:  Length: 2, Width: 2, Height: 3  Child 1:  Length: 1, Width: 4, Height: 4  Child 2:  Length: 1, Width: 3, Height: 4  Child 3:  Length: 1, Width: 3, Height: 2 Example Genomes Note: Each node also contains the neuron data, but since this cannot be seen physically I did not list it

5. Methodology: Controller  Controller object maintains an array of genomes  Generates new genomes at beginning of simulation  Displays creatures and runs simulation  Measures fitness and breeds creatures for next generation

6. Methodology: Nodes  Physical dimensions  Where the body segment connects to its parent and children segments  Neuron functions  Methods to add children and connection points  Accessor methods return children, dimensions and neurons

7. Methodology: Creature GA  Applies neuron functions to sensor values  Possible neuron functions:  Oscillating functions: sin, cos, atan, saw- wave  Other functions: sum-threshold, sign-of, min, max, mem, log, expt, devide, interpolate, differentiate  Returns joint velocity values for the associated body segment

8. Circulation of Data Values received from joint-angle sensors Values sent to the GA, where they are put through node's neurons Values become effectors and modify joint velocity

9. Reproduction  Creatures are evaluated based on their performance in the simulation  Top half of genomes copied directly (asexual)‏  Remaining 1/2 of genomes are crossed over in pairs

10. Crossover

11.  Children have both physical and control traits of their parents  A portion of the limbs will be physically the same and controlled in the same way as one of the parents, while the remaining limbs will be identical to the second parent  Allows for co-evolution: both the body and brain change

12. Simulation Process  Population is simulated in the physical environment  All of the creatures are displayed at once  Final fitnesses are evaluated and reproduced  Repeated with next generation for n generations

13. Example Simulation One creature is copied directly to the second generation. Can you tell which one? http://www.youtube.com/watch?v=a_jyKuzrBRM

14. Testing  Fitness tests  Measures the success of a motor method  Progression of fitness level shows evolution of technique  Create graph of fitness level over time

15. Results  Results had a consistent upwards trend, but varied on the amount of improvement  Some graphs showed a levelling off or decrease in the later generations (100+), but after adding mutation this rarely happened

16. Graphs

17. Potential Creatures