Automatic Content Generation in the Galactic Arms Race Video Game

摘要

Simulation and game content includes the levels, models, textures, items, and other objects encountered and possessed by players during the game. In most modern video games and in simulation software, the set of content shipped with the product is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly and automatically renewed, players would remain engaged longer. This paper introduces two novel technologies that take steps toward achieving this ambition: 1) a new algorithm called content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) is introduced that automatically generates graphical and game content while the game is played, based on the past preferences of the players, and 2) Galactic Arms Race (GAR), a multiplayer video game, is constructed to demonstrate automatic content generation in a real online gaming platform. In GAR, which is available to the public and playable online, players pilot space ships and fight enemies to acquire unique particle system weapons that are automatically evolved by the cgNEAT algorithm. A study of the behavior and results from over 1000 registered online players shows that cgNEAT indeed enables players to discover a wide variety of appealing content that is not only novel, but also based on and extended from previous content that they preferred in the past. Thus, GAR is the first demonstration of evolutionary content generation in an online multiplayer game. The implication is that with cgNEAT it is now possible to create applications that generate their own content to satisfy users, potentially reducing the cost of content creation and increasing entertainment value from single-player to massively multiplayer online games (MMOGs) with a constant stream of evolving content.