Title

Evolving Content In The Galactic Arms Race Video Game

Abstract

Video game content includes the levels, models, items, weapons, and other objects encountered and wielded by players during the game. In most modern video games, the set of content shipped with the game is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly renewed, players would remain engaged longer in the evolving stream of novel content. To realize this ambition, this paper introduces the content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) algorithm, which automatically evolves game content based on player preferences, as the game is played. To demonstrate this approach, the Galactic Arms Race (GAR) video game is also introduced. In GAR, players pilot space ships and fight enemies to acquire unique particle system weapons that are evolved by the game. As shown in this paper, players can discover a wide variety of content that is not only novel, but also based on and extended from previous content that they preferred in the past. The implication is that it is now possible to create games that generate their own content to satisfy players, potentially significantly reducing the cost of content creation and increasing the replay value of games. ©2009 IEEE.

Publication Date

12-14-2009

Publication Title

CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games

Number of Pages

241-248

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CIG.2009.5286468

Socpus ID

71549136474 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/71549136474

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