Supervisor

iAbstract This master thesis presents an approach for how to implement an Evolution-ary Multi-Agent Potential Field (EMAPF) based AI in small scale combat (SSC) scenarios in real-time strategy (RTS) computer games. The thesis will show how evolutionary algorithms can be used to auto tune and optimize potential field param-eters for making unit agents able to navigate and hunt down enemy squads/troops in an efficient way in an unknown environment. The very popular top-selling RTS game StarCraft: Brood War has been chosen as test platform, mainly because the game is extremely well balanced and has a complexity in the game world, which makes the development of AI’s very challeng-ing. Since StarCraft is not open-source, the free open-source framework Brood War Application Programming Interface (BWAPI) has been used to communicate with the game. In order to make the AI able to control unit agents moving around in the game world, they will be effected by different types of potential fields placed in bot