Expanding Expressive Range: Evaluation Methodologies for Procedural Content Generation
Expanding Expressive Range: Evaluation Methodologies for Procedural Content Generation
复制标题
扩大表达范围：程序内容生成的评估方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Summerville
中科院分区：
文献类型：
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作者：
A. Summerville
程序内容生成（PCG）是视频游戏的一部分，在过去的十年中一直是一个活跃的研究领域。然而，尽管对PCG感兴趣，但没有公认的方法来评估和分析发生器。此外，机器学习PCG技术的最新趋势通常陈述了在原始内容内学习设计的目标，但很少评估这些技术是否实际实现了这一目标。本文介绍了许多用于PCG系统评估和分析的技术，使从业者和研究人员能够更好地了解这些系统的优点和缺点，更好地比较系统，并减少对ad-hoc、cherry-picking-prone技术的依赖。
Procedural Content Generation (PCG) has been a part of video games for the majority of their existence and have been an area of active research over the past decade. How- ever, despite the interest in PCG there is no commonly ac- cepted methodology for assessing and analyzing a generator. Furthermore, the recent trend towards machine learned PCG techniques commonly state the goal of learning the design within the original content, but there has been little assess- ment of whether these techniques actually achieve this goal. This paper presents a number of techniques for the assess- ment and analysis of PCG systems, allowing practitioners and researchers better insight into the strengths and weaknesses of these systems, allowing for better comparison of systems, and reducing the reliance on ad-hoc, cherry-picking-prone tech- niques.