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- 1. 1 Evolving the strategies of agents for the ANTS Game J. Carpio, P. García-Sánchez, A.M. Mora, J.J. Merelo, J. Caraballo1, F. Vaz , C. Cotta IWANN 2013, Tenerife, 12-14 July
- 2. 2 Agenda ● RTS Games ● Google AI Challenge ● ANTS game ● Bot Behaviour ● Parameters to optimize ● Fitness used ● Experimental Setup ● Results ● Conclusions
- 3. 3 RTS Games ● Real-Time Strategy Games ● Several units distributed in a playing arena that competes for resources ● Examples: StarCraft, WarCraft, Age of Empires
- 4. 4 Google AI Challenge ● Create AIs for RTS games: TRON (2010), Planet Wars (2011) , and ANTS (2012) ● Each submission competes with the others in the Google server
- 5. 5 ANTS Game ● Objective: conquer all the anthills of the enemy ● Surround enemy's ants to kill them ● Collect food to create more ants ● Restrictions – Algorithms can not use previous fighting information. – Only a second per turn to move the ants colony.
- 6. 6 Maps
- 7. 7 Bot Behaviour IF enemy hill in sight attack the hill ELSE IF food in sight pick up the food ELSE IF enemy ants in sight attack the ants ELSE IF non-explored zone in sight explore the area randomly
- 8. 8 Parameters to optimize with the GA ● Food distance ● Time remaining margin ● Distances to hill/ant ● Turns during left mode
- 9. 9 Fitness used ● Standard: using only the “score” (number of anthills conquered) ● Hierarchical: a tuple with [score, -enemy's score, number of ants, -number of enemy's ants]
- 10. 10 Experimental Setup ● Six different maps have been used ● Each fitness evaluation is made 10 times ● 64 individuals ● 0,3 crossover rate ● Pool of 32 best parents ● After the training we compare versus the winner of the competition and positions 165 and 993.
- 11. 11 Results ● Prior to optimization of the parameters, our bot ended in position 2076. ● But after optimization, it wins the bot in position 993 ● Number 1 and 163 were very competitive, but using the optimized parameters with the hierarchical fitness increased the number of ants and decreased the enemy's ant
- 12. 12 Conclusions ● A simple agent can be optimized to win better opponents ● Hierarchical fitness increases the chances to win (adding more information) ● The strategy depends of the enemies ● Future work: map analysis, online adaptation, analysis of the enemies...
- 13. 13 Thanks!