Evolving maps for match balancing in first person shooters

Match balancing is one of the most important design issues in the development of an adversarial multiplayer shooter. Therefore, matchmaking algorithms are generally used to build teams, such that players can have fun with each other and enjoy the game experience. In this work we approach this problem from a completely different angle: we show that the design of the game content has a large impact on the match balancing and that procedural content generation might be a promising approach to improve it. In particular, we present a methodology to evolve maps for Cube 2: Saubertran, an open source first person shooter, and to improve the game balancing for specific combinations of players skills and strategies. We tested our approach on three different scenarios that involve players with different skill levels as well as players using completely different weapons. Our results are very promising and show that, in all the scenarios considered, our approach is able to evolve maps that result in a balanced game experience.