1. CAP6938 Neuroevolution and Artificial Embryogeny Real-time NEAT Dr. Kenneth Stanley February 22, 2006

2. Generations May Not Always Be Appropriate When a population is evaluated simultaneously –Many are observable at the same time –Therefore, entire population would change at once –A sudden change is incongruous, highly noticeable When a human interacts with one individual at a time –Want things to improve constantly

3. Steady State GA: One Individual Is Replaced at a Time Start by evaluating entire first generation Then continually pick one to remove, replace it with child of the best Start: Evaluate All 1) Remove poor individual 2) Create offpsring from good parents 3) Replace removed individual Repeat…

4. Steady State During Simultaneous Evaluation: Similar but not Identical Several new issues when evolution is real-time –Evaluation is asynchronous –When to replace? –How to assign fitness? –How to display changes

5. Regular NEAT Introduces Additional Challenges for Real Time Speciation equations based on generations No “remove worst” operation defined in algorithm Dynamic compatibility thresholding assumes generations

6. Speciation Equations Based on Generations

7. How to Remove the Worst? No such operation in generational NEAT Worst often may often be a new species –Removing it would destroy protection of innovation –Loss of regular NEAT dynamics

8. Dynamic Compatibility Thresholding Assumes A Next Generation

9. Real-time NEAT Addresses Both the Steady State and Simultaneity Issues Real-time speciation Simultaneous and asynchronous evaluation Steady state replacement Fast enough to change while a game is played Equivalent dynamics to regular NEAT

10. Main Loop (Non-Generational)

11. Choosing the Parent Species

12. Finally: How Many Ticks Between Replacements? Intuitions: –The more often replacement occurs, the fewer are eligible –The larger the population, the more are eligible –The high the age of maturity, the fewer are eligible

13. rtNEAT Is Implemented In NERO Download at http://nerogame.orghttp://nerogame.org rtNEAT source soon available (TBA) Simulated demos have public appeal –Over 50,000 downloads –Appeared on Slashdot –Best Paper Award in Computational Intelligence and Games –Independent Games Festival Best Student Game Award –rtNEAT licensed –Worldwide media coverage

14. NERO: NeuroEvolving Robotic Operatives NPCs improve in real time as game is played Player can train AI for goal and style of play Each AI Unit Has Unique NN

15. NERO Battle Mode After training, evolved behaviors are saved Player assembles team of trained agents Team is tested in battle against opponent’s team

16. NERO Training: The Factory Reduces noise during evaluation –All evaluations start out similarly Robot bodies produced by “factory” Each body sent back to factory to respawn Bodies retain their NN unless chosen for replacement NN’s have different ages –Fitness is diminishing average of spawn trials:

17. NERO Inputs and Outputs

18. Enemy/Friend Radars

19. Enemy On-Target Sensor

20. Object Rangefinder Sensors

21. Enemy Line-of-Fire Sensors

22. Further Applications? New kinds of games New kinds of AI in games New kinds of real-time simulations Training applications Interactive steady-state evolution

23. Next Topic: Improving the neural model Adaptive neural networks Change over a lifetime Leaky integrator neurons and CTRNN Homework due 2/27/06: Working genotype to phenotype mapping. Genetic representation completed. Saving and loading of genome file I/O functions completed. Turn in summary, code, and examples demonstrating that it works. Evolutionary Robots with On-line Self-Organization and Behavioral FitnessEvolutionary Robots with On-line Self-Organization and Behavioral Fitness by Dario Floreano and Joseba Urzelai (2000) Evolving Adaptive Neural Networks with and Without Adaptive Synapses by Kenneth O. Stanley, Bobby D. Bryant, and Risto Miikkulainen (2003) Evolving Adaptive Neural Networks with and Without Adaptive Synapses

24. Project Milestones (25% of grade) 2/6: Initial proposal and project description 2/15: Domain and phenotype code and examples 2/27: Genes and Genotype to Phenotype mapping 3/8: Genetic operators all working 3/27: Population level and main loop working 4/10: Final project and presentation due (75% of grade)