Demonstrating Automatic Content Generation in the Galactic Arms Race Video Game

Abstract

In most modern video games, content (e.g. models, levels, weapons, etc.) shipped with the game is static and unchanging, or at best, randomized within a narrow set of parameters. However, if game content could be constantly renewed, players would remain engaged longer. To realize this ambition, the content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) algorithm automatically evolves novel game content based on player preferences, as the game is played. To demonstrate this approach, the Galactic Arms Race (GAR) video game, which incorporates cgNEAT, will be presented. In GAR, players pilot space ships and fight enemies to acquire novel particle system weapons that are evolved by the game. The live demo will show how GAR players can discover a wide variety of weapons that are not only novel, but also based on and extended from previous content that they preferred in the past. The implication of cgNEAT is that it is now possible to create games that generate their own content, potentially significantly reducing the cost of content creation and increasing the replay value of games.