Bayesian Networks for Micromanagement Decision Imitation in the RTS Game Starcraft

Abstract

Real time strategy (RTS) games provide various research areas for Artificial Intelligence. One of these areas involves the management of either individual or small group of units, called micromanagement. This research provides an approach that implements an imitation of the player's decisions as a mean for micromanagement combat in the RTS game Starcraft. A bayesian network is generated to fit the decisions taken by a player and then trained with information gather from the player's combat micromanagement. Then, this network is implemented on the game in order to enhance the performance of the game's built-in Artificial Intelligence module. Moreover, as the increase in performance is directly related to the player's game, it enriches the player's gaming experience. The results obtained proved that imitation through the implementation of bayesian networks can be achieved. Consequently, this provided an increase in the performance compared to the one presented by the game's built-in AI module.