论文标题
使用卷积神经网络嵌入可视化生成空间
Visualising Generative Spaces Using Convolutional Neural Network Embeddings
论文作者
论文摘要
随着对游戏的程序内容产生（PCG）的学术兴趣增加了，因此需要方法学来比较和对比替代PCG系统的输出空间。在本文中，我们使用从训练有素的卷积神经网络中提取的嵌入式介绍并评估了一种可视化水平生成系统生成空间的新方法。我们根据其产生与其行为特征相关的编码游戏水平的2D可视化的能力来评估方法。在两个替代游戏域分别超级马里奥和盒子的替代游戏域中的结果表明，这种方法在某些设置中具有强大的功能，并且有可能取代视觉上比较生成空间的替代方法。然而，在这项工作中调查的整个领域，并且容易受到间歇性失败的影响。我们得出的结论是，这种方法值得进一步评估，但是未来的实施将受益于重大改进。
As academic interest in procedural content generation (PCG) for games has increased, so has the need for methodologies for comparing and contrasting the output spaces of alternative PCG systems. In this paper we introduce and evaluate a novel approach for visualising the generative spaces of level generation systems, using embeddings extracted from a trained convolutional neural network. We evaluate the approach in terms of its ability to produce 2D visualisations of encoded game levels that correlate with their behavioural characteristics. The results across two alternative game domains, Super Mario and Boxoban, indicate that this approach is powerful in certain settings and that it has the potential to supersede alternative methods for visually comparing generative spaces. However its performance was also inconsistent across the domains investigated in this work, as well as it being susceptible to intermittent failure. We conclude that this method is worthy of further evaluation, but that future implementations of it would benefit from significant refinement.
