Evolving strategies for playing Galcon
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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)
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Optimizing player behavior in a real-time strategygame using evolutionary algorithmsA. Fernández-Ares, A.M. Mora, J.J. Merelo, P. Garcí́a-Sánchez and C. Fernandes
Transcript of 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. 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)
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Galcon
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Google AI Challenge
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Game conditions
Decision time restricted to 1 second
Impossible to keep state from one step to tne next
Full knowledge of own and other's state
And Physics of the game
Swiss-style tournament
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Baseline: GoogleBot
Planet with most ships chosen as base for attack
Planet to attack chosen according to growth rate and difference
Only one attack at the time
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Aresbot
Hand-designed to beat GoogleBot
Similar, but
Colonies send ships to base (tithe)
If they are close to attack planet, they send attack directly
Same planet can't be attacked twice
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But we can do better!
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GeneBot
Let an evolutionary algorithm evolve constant and probabilities for AresBot
Use standard GA
1-elitism
BLX-α crossover
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Several maps used to train bot
Chosen for representing different characteristics
Base planet relative position
Excentricity
If a bot is able to beat GoogleBot there, it will probably always will
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Designing a fitness function
Important factors: number of victories and turns needed to win them
Victories are rather a constraint, so it's not multiobjective
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Results
Genebot is able to defeat GoogleBot in the 100 maps provided by Google
Strategy completely different from AresBot
Tithe and support attack less likely, but with more ships
Growth rate fo target ship becomes more important
Many more ships sent to target planet
Beats GoogleBot in 30% less time than AresBot
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Comparing bots
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Did we win?
20% best: position 1454
Not bad, not great
Evolved strategy can go only as far as the underlying one.
Genetic algorithm was not really optimized
Future work.
Forget contest restrictions
Leave strategy more open
Be more “genetic”
