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Automatic graphics and game content generation through evolutionary computation.

机译:通过进化计算自动生成图形和游戏内容。

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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 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 in the evolving stream of content. This dissertation introduces three novel technologies that together realize this ambition. (1) The first, NEAT Particles, is an evolutionary method to enable users to quickly and easily create complex particle effects through a simple interactive evolutionary computation (IEC) interface. That way, particle effects become an evolvable class of content, which is exploited in the remainder of the dissertation. In particular, (2) 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. Through cgNEAT, the game platform on its own can generate novel content that is designed to satisfy its players. Finally, (3) the Galactic Arms Race (GAR) multiplayer online video game is constructed to demonstrate these techniques working on a real online gaming platform. In GAR, which was made 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. The resulting study 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. The implication is that with cgNEAT it is now possible to create applications that generate their own content to satisfy users, potentially significantly reducing the cost of content creation and considerably increasing entertainment value with a constant stream of evolving content.
机译:模拟和游戏内容包括游戏过程中玩家遇到和拥有的级别,模型,纹理,物品和其他对象。在大多数现代视频游戏和模拟软件中,产品附带的内容集是静态的,并且保持不变,或者充其量只能在狭窄的参数集内随机进行。但是,理想情况下,如果可以不断自动地更新游戏内容,则玩家将可以更长时间地参与不断发展的内容流。本文介绍了三种实现这一目标的新颖技术。 (1)第一种是NEAT粒子,是一种进化方法,使用户可以通过简单的交互式进化计算(IEC)界面快速轻松地创建复杂的粒子效果。这样,粒子效果成为内容的可演化类,在本文的其余部分中将对此加以利用。特别是(2)引入了一种新的算法,称为内容生成增强神经元进化(cgNEAT),该算法根据玩家过去的偏好自动在玩游戏时生成图形和游戏内容。通过cgNEAT,游戏平台本身可以生成旨在满足其玩家需求的新颖内容。最后,(3)构建了Galactic Arms Race(GAR)多人在线视频游戏,以演示这些技术在真实的在线游戏平台上的工作。在GAR(已向公众公开并可以在线玩)中,玩家驾驶太空飞船并与敌人作战,以获取独特的粒子系统武器,这些武器会通过cgNEAT算法自动演化。最终的研究结果表明,cgNEAT确实使玩家能够发现各种新颖的内容,不仅新颖,而且基于他们过去喜欢的先前内容并从中扩展而来。这意味着使用cgNEAT现在可以创建生成自己的内容以满足用户需求的应用程序,从而潜在地显着降低内容创建成本,并通过不断发展的内容流显着提高娱乐价值。

著录项

  • 作者

    Hastings, Erin.;

  • 作者单位

    University of Central Florida.;

  • 授予单位 University of Central Florida.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 168 p.
  • 总页数 168
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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