Evolving strategies for playing Galcon


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Optimizing player behavior in a real-time strategy
game using evolutionary algorithms
A. Fernández-Ares, A.M. Mora, J.J. Merelo, P. Garcí́a-Sánchez and C. Fernandes

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  • Background from http://www.flickr.com/photos/makelessnoise/562879634/in/photostream/ (MakeLessNoise)
  • Picture from http://www.flickr.com/photos/keees/1967279/in/photostream/
  • This is Ares after having a bad nosejob http://www.flickr.com/photos/thisisbossi/3056747661/in/photostream/ Ares happens to be also the second last name, or the last-last name of one of the authors.
  • Picture from http://www.flickr.com/photos/pmiaki/5107420450/in/photostream/
  • http://www.flickr.com/photos/docpopular/4381307706/in/photostream/
  • Evolving strategies for playing Galcon

    1. 1. Optimizing player behavior in a real-time strategy game using evolutionary algorithms A. Fernández-Ares, A.M. Mora, J.J. Merelo , P. García-Sánchez and C. Fernandes GeNeura group: http://geneura.wordpress.com http://twitter.com/geneura Departamento de Arquitectura y Tecnología de Computadores University of Granada (Spain)
    2. 2. Galcon
    3. 3. Google AI Challenge
    4. 4. Game conditions <ul><li>Decision time restricted to 1 second
    5. 5. Impossible to keep state from one step to tne next
    6. 6. Full knowledge of own and other's state </li><ul><li>And Physics of the game </li></ul><li>Swiss-style tournament </li></ul>
    7. 7. Baseline: GoogleBot <ul><li>Planet with most ships chosen as base for attack
    8. 8. Planet to attack chosen according to growth rate and difference
    9. 9. Only one attack at the time </li></ul>
    10. 10. Aresbot <ul><li>Hand-designed to beat GoogleBot
    11. 11. Similar, but </li><ul><li>Colonies send ships to base ( tithe )
    12. 12. If they are close to attack planet, they send attack directly
    13. 13. Same planet can't be attacked twice </li></ul></ul>
    14. 14. But we can do better!
    15. 15. GeneBot <ul><li>Let an evolutionary algorithm evolve constant and probabilities for AresBot
    16. 16. Use standard GA </li><ul><li>1-elitism
    17. 17. BLX- α crossover </li></ul></ul>
    18. 18. Several maps used to train bot <ul><li>Chosen for representing different characteristics </li><ul><li>Base planet relative position
    19. 19. Excentricity </li></ul><li>If a bot is able to beat GoogleBot there, it will probably always will </li></ul>
    20. 20. Designing a fitness function <ul><li>Important factors: number of victories and turns needed to win them </li><ul><li>Victories are rather a constraint, so it's not multiobjective </li></ul></ul>
    21. 21. Results <ul><li>Genebot is able to defeat GoogleBot in the 100 maps provided by Google
    22. 22. Strategy completely different from AresBot </li><ul><li>Tithe and support attack less likely, but with more ships
    23. 23. Growth rate fo target ship becomes more important
    24. 24. Many more ships sent to target planet </li></ul><li>Beats GoogleBot in 30% less time than AresBot </li></ul>
    25. 25. Comparing bots
    26. 26. Did we win? <ul><li>20% best: position 1454 </li><ul><li>Not bad, not great </li></ul><li>Evolved strategy can go only as far as the underlying one.
    27. 27. Genetic algorithm was not really optimized
    28. 28. Future work. </li><ul><li>Forget contest restrictions
    29. 29. Leave strategy more open
    30. 30. Be more “genetic” </li></ul></ul>
    31. 31. Thanks for your attention - Questions? [email_address] http://twitter.com/jjmerelo
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