论文标题
通过示例驱动的BSP和变异自动编码器，多域级生成并与草图混合
Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
论文作者
论文摘要
通过机器学习（PCGML）生成程序内容已经证明了其作为内容和游戏创建方法的有用性，并已被证明能够支持人类的创造力。创造力的一个重要方面是结合创造力或跨领域之间思想和概念的重组，适应和重新组合和重复使用。在本文中，我们提出了一种用于水平生成的PCGML方法，该方法能够从多个域重新组合，适应和重复使用结构模式到近似看不见的域。我们扩展了涉及示例驱动的二进制空间分配的先前工作，以重新组合和重复使用多个域中的模式，并结合了变异自动编码器（VAE）来生成看不见的结构。我们通过在这些域的$ 7 $域和子集上混合来评估我们的方法。我们表明，我们的方法能够在保留结构组件的同时将域混合在一起。此外，通过使用不同的训练域，我们的方法能够生成1）重现和捕获目标域特征的水平，以及2）与输入域具有巨大属性的级别。
Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across $7$ domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.
