论文标题
有条件的水平生成和游戏混合
Conditional Level Generation and Game Blending
论文作者
论文摘要
先前的研究表明，通过学习现有级别数据的潜在表示，变异自动编码器（VAE）对于生成和混合游戏水平非常有用。我们通过探索有条件VAE（CVAE）启用的级别设计和应用程序来构建此类模型。 CVAE通过允许使用标记数据对其进行训练，从而增强它们，从而使输出能够在某些输入上生成。我们研究了水平生成过程中的控制量增加以及通过标记的游戏级别数据培训产生所需输出的能力如何建立在先前的PCGML方法上。通过我们对超级马里奥兄弟，Kid Icarus和Mega Man的水平培训CVAE的结果，我们表明，这样的模型可以通过生成具有所需级别元素和模式的水平来协助水平设计，并与游戏组合产生混合水平。
Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data. We build on such models by exploring the level design affordances and applications enabled by conditional VAEs (CVAEs). CVAEs augment VAEs by allowing them to be trained using labeled data, thus enabling outputs to be generated conditioned on some input. We studied how increased control in the level generation process and the ability to produce desired outputs via training on labeled game level data could build on prior PCGML methods. Through our results of training CVAEs on levels from Super Mario Bros., Kid Icarus and Mega Man, we show that such models can assist in level design by generating levels with desired level elements and patterns as well as producing blended levels with desired combinations of games.
