CAP6938 Neuroevolution and Artificial Embryogeny Competitive Coevolution

1. CAP6938Neuroevolution and Artificial EmbryogenyCompetitive 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.

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 Coevolution by De Jong, E.D. and J.B. Pollack (2004)Monotonic Solution Concepts in Coevolution by Ficici, Sevan G. (2005)

27. Next Topic: Real-time NEAT (rtNEAT) • Simultaneous and asynchronous evaluation • Non-generational • Useful in video games and simulations • NERO: Video game with rtNEAT -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) -http://Nerogame.org -Extra coevolution papers 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.

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)