  This paper presents a novel approach to procedural generation of urban maps
for First Person Shooter (FPS) games. A multi-agent evolutionary system is
employed to place streets, buildings and other items inside the Unity3D game
engine, resulting in playable video game levels. A computational agent is
trained using machine learning techniques to capture the intent of the game
designer as part of the multi-agent system, and to enable a semi-automated
aesthetic selection for the underlying genetic algorithm.
