Hierarchically Composing Level Generators for the Creation of Complex Structures

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Games Pub Date : 2023-07-21 DOI:10.1109/TG.2023.3297619
Michael Beukman;Manuel Fokam;Marcel Kruger;Guy Axelrod;Muhammad Nasir;Branden Ingram;Benjamin Rosman;Steven James
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Abstract

Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimizable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in the industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimizable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a noncompositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in Minecraft ) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.
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创建复杂结构的分层合成级发生器
程序内容生成(PCG)是一个不断发展的领域,在视频游戏行业有大量应用,并具有巨大的潜力,可以帮助人们以手工制作的一小部分成本制作出更好的游戏。然而,程序内容生成的大部分工作都集中在生成简单游戏中相对简单的关卡上,因为为复杂设置设计可优化的目标函数具有挑战性。这就限制了 PCG 对更复杂、更现代游戏的适用性,阻碍了 PCG 在业界的应用。我们的工作旨在通过引入一种组成式关卡生成方法来解决这一限制,这种方法可以递归地组成简单的低级生成器,从而构建大型复杂的作品。这种方法可以轻松优化目标,并通过引用低级组件,以可解释的方式设计复杂结构。我们通过实证证明,我们的方法能更准确地满足设计者在多项任务中的功能要求,因此优于非组合式基线方法。最后,我们提供了一个定性展示(在 Minecraft 中),说明了使用简单的基础生成器生成的大型复杂但仍然连贯的结构。
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来源期刊
IEEE Transactions on Games
IEEE Transactions on Games Engineering-Electrical and Electronic Engineering
CiteScore
4.60
自引率
8.70%
发文量
87
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Table of Contents IEEE Computational Intelligence Society Information IEEE Transactions on Games Publication Information Large Language Models and Games: A Survey and Roadmap Investigating Efficiency of Free-For-All Models in a Matchmaking Context
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