In the current state of the video game productions, most of the video game levels are created by the human operators working as level designers. This manual process is not only time-consuming and resource-intensive but also hard to guarantee uniform quality in the contents created by the level designers. One way to address this issue is to use computer-assisted level design techniques. In this paper, we have proposed a novel framework for computer-assisted video game level design that leverages neural networks, particularly generative adversarial networks (GANs) and autoencoders. The general idea is to learn over a dataset of high-quality levels and subsequently improve the ones created by the level designers. The proposed method is independent of the graphical dimensionality of the game and will work for 2D and 3D games in general. The autoencoder is used to create an intermediate representation of the level that is itself changed using the backpropagation technique according to the feedback obtained by feeding the output of the autoencoder to the discriminator component of the GAN. After performing a series of evaluations on the proposed framework and by automatically improving a series of purposefully corrupted game levels, the results demonstrate a noticeable improvement compared with the usage of simple autoencoders used to improve the video game levels in the previous researches.

中文翻译：

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利用深度神经网络评估和校正手动设计的游戏水平的方法

在视频游戏产品的当前状态下，大多数视频游戏关卡是由作为关卡设计人员的人工操作员创建的。该手动过程不仅耗时且耗费资源，而且难以保证关卡设计人员创建的内容具有统一的质量。解决此问题的一种方法是使用计算机辅助的关卡设计技术。在本文中，我们提出了一种用于计算机辅助视频游戏级设计的新颖框架，该框架利用了神经网络，尤其是生成对抗网络（GAN）和自动编码器。总体思路是学习高质量的关卡数据集，然后改进关卡设计人员创建的数据集。所提出的方法与游戏的图形尺寸无关，并且通常将适用于2D和3D游戏。根据通过将自动编码器的输出馈送到GAN的鉴别器组件而获得的反馈，自动编码器用于创建电平的中间表示，该电平本身使用反向传播技术进行了更改。在对提出的框架进行了一系列评估并通过自动改善一系列有意破坏的游戏级别之后，与先前研究中用于改善视频游戏级别的简单自动编码器的使用相比，结果证明了明显的改进。