Procedural urban environments for FPS games

TL;DR

This paper introduces a new method for generating urban maps in FPS games using a multi-agent evolutionary system within Unity3D, incorporating machine learning to align with designer intent and automate aesthetic choices.

Contribution

It presents a novel multi-agent evolutionary framework that integrates machine learning for procedural urban environment generation in FPS games.

Findings

Generated playable urban maps within Unity3D

Machine learning captures designer intent effectively

Automated aesthetic selection improves map quality

Abstract

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.