通过创造性机器学习实现游戏设计（GDCML）
Towards Game Design via Creative Machine Learning (GDCML)
摘要：
近年来，机器学习（ML）系统越来越多地应用于执行创造性任务。这种创造性的ML方法已经在视觉艺术和音乐领域广泛使用，用于图像和音乐生成以及风格转换等应用。然而，尽管出现了基于ML的游戏内容生成方法，但类似的创造性ML技术在游戏设计领域并没有得到广泛采用。在本文中，我们主张利用和重新利用这些创造性的技术来设计游戏内容，将其称为通过Creative ML（GDCML）进行游戏设计的方法。我们重点介绍了支持GDCML的现有系统，并说明了创造性ML如何通过示例应用程序和拟议的系统为新系统提供信息。
英文摘要：
In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks. Such creative ML approaches have seen wide use in the domains of visual art and music for applications such as image and music generation and style transfer. However, similar creative ML techniques have not been as widely adopted in the domain of game design despite the emergence of ML-based methods for generating game content. In this paper, we argue for leveraging and repurposing such creative techniques for designing content for games, referring to these as approaches for Game Design via Creative ML (GDCML). We highlight existing systems that enable GDCML and illustrate how creative ML can inform new systems via example applications and a proposed system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
