Evolving effective micro behaviors in RTS game

We investigate using genetic algorithms to generate high quality micro management in combat scenarios for real-time strategy games. Macro and micro management are two key aspects of real-time strategy games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes against equal numbers and types of opponent units or win even when outnumbered. In this paper, we use influence maps and potential fields to generate micro management positioning and movement tactics. Micro behaviors are compactly encoded into fourteen parameters and we use genetic algorithms to search for effective micro management tactics for the given units. We tested the performance of our ECSLBot (the evolved player), obtained in this way against the default StarCraft AI, and two other state of the art bots, UAlbertaBot and Nova on several skirmish scenarios. The results show that the ECSLBot tuned by genetic algorithms outperforms the UAlbertaBot and Nova in kiting efficiency, target selection, and knowing when to flee to survive. We believe our approach is easy to extend to other types of units and can be easily adopted by other AI bots.