1. CAP6938 Neuroevolution and Artificial Embryogeny Competitive Coevolution Dr. Kenneth Stanley February 20, 2006

2. Example: I Want to Evolve a Go Player Go is one of the hardest games for computers I am terrible at it There are no good Go programs either (hypothetically) I have no idea how to measure the fitness of a Go player How can I make evolution solve this problem?

3. Generally: Fitness May Be Difficult to Formalize Optimal policy in competitive domains unknown Only winner and loser can be easily determined What can be done?

4. Competitive Coevolution Coevolution: No absolute fitness function Fitness depends on direct comparisons with other evolving agents Hope to discover solutions beyond the ability of fitness to describe Competition should lead to an escalating arms race

5. The Arms Race

6. The Arms Race is an AI Dream Computer plays itself and becomes champion No need for human knowledge whatsoever In practice, progress eventually stagnates (Darwen 1996; Floreano and Nolfi 1997; Rosin and Belew 1997)

7. So Who Plays Against Whom? If evaluation is expensive, everyone can’t play everyone Even if they could, a lot of candidates might be very poor If not everyone, who then is chosen as competition for each candidate? Need some kind of intelligent sampling

8. Challenges with Choosing the Right Opponents Red Queen Effect: Running in Circles –A dominates B –C dominates B –A dominates B Overspecialization –Optimizing a single skill to the neglect of all others –Likely to happen without diverse opponents in sample Several other failure dynamics

9. Heuristic in NEAT: Utilize Species Champions Each individual plays all the species champions and keeps a score

10. Hall of Fame (HOF) (Rosin and Belew 1997) Keep around a list of past champions Add them to the mix of opponents If HOF gets too big, sample from it

11. More Recently: Pareto Coevolution Separate learners and tests The tests are rewarded for distinguishing learners from each other The learners are ranked in Pareto layers –Each test is an objective –If X wins against a superset of tests that Y wins again, then X Pareto-dominates Y –The first layer is a nondominated front –Think of tests as objectives in a multiobjective optimization problem Potentially costly: All learners play all tests De Jong, E.D. and J.B. Pollack (2004). Ideal Evaluation from Coevolution Evolutionary Computation, Vol. 12, Issue 2, pp. 159-192, published by The MIT Press.Ideal Evaluation from Coevolution The MIT Press

12. Choosing Opponents Isn’t Everything How can new solutions be continually created that maintain existing capabilities? Mutations that lead to innovations could simultaneously lead to losses What kind of process ensures elaboration over alteration?

13. Alteration vs. Elaboration

14. Answer: Complexification Fixed-length genomes limit progress Dominant strategies that utilize the entire genome must alter and thereby sacrifice prior functionality If new genes can be added, dominant strategies can be elaborated, maintaining existing capabilities

15. Test Domain: Robot Duel Robot with higher energy wins by colliding with opponent Moving costs energy Collecting food replenishes energy Complex task: When to forage/save energy, avoid/pursue?

16. Robot Neural Networks

17. Experimental Setup 13 complexifying runs, 15 fixed-topology runs 500 generations per run 2-population coevolution with hall of fame (Rosin & Belew 1997)

18. Performance is Difficult to Evaluate in Coevolution How can you tell if things are improving when everything is relative? –Number of wins is relative to each generation No absolute measure is available No benchmark is comprehensive

19. Expensive Method: Master Tournament (Cliff and Miller 1995; Floreano and Nolfi 1997) Compare all generation champions to each other Requires n^2 evaluations –An accurate evaluation may involve e.g. 288 games Defeating more champions does not establish superiority

20. Strict and Efficient Performance Measure: Dominance Tournament (Stanley & Miikkulainen 2002)

21. Result: Evolution of Complexity As dominance increases so does complexity on average Networks with strictly superior strategies are more complex

22. Comparing Performance

23. Summary of Performance Comparisons

24. The Superchamp

25. Cooperative Coevolution Groups attempt to work with each other instead of against each other But sometimes it’s not clear what’s cooperation and what’s competition Maybe competitive/cooperative is not the best distinction? –Newer idea: Compositional vs. test-based

26. Summary Picking best opponents Maintaining and elaborating on strategies Measuring performance Different types of coevolution Advanced papers on coevolution: Ideal Evaluation from CoevolutionIdeal Evaluation from Coevolution by De Jong, E.D. and J.B. Pollack (2004) Monotonic Solution Concepts in Coevolution by Ficici, Sevan G. (2005) Monotonic Solution Concepts in Coevolution

27. Next Topic: Real-time NEAT (rtNEAT) Simultaneous and asynchronous evaluation Non-generational Useful in video games and simulations NERO: Video game with rtNEAT 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. -Shorter symposium paper: Evolving Neural Network Agents in the NERO Video Game by Kenneth O. Stanley and Risto Miikkulainen (2005) -Optional journal (longer, more detailed) paper: Real-time Neuroevolution in the NERO Video Game by Kenneth O. Stanley and Risto Miikkulainen (2005)Evolving Neural Network Agents in the NERO Video Game Real-time Neuroevolution in the NERO Video Game -http://Nerogame.orghttp://Nerogame.org -Extra coevolution papers

28. 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)