用反例提升游戏关卡生成的可控性，验证负样本对GAN的效果
Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models
- 通过正负样本联合训练，增强GAN对目标关卡特征的学习
- 引入负例后，生成关卡的可玩性和可控性显著提升
- 适合游戏设计自动化与生成式AI研究者参考
生成对抗网络（GAN）是无监督模型，用于学习并复制目标分布。原始GAN可扩展为更可控的模型。条件GAN（CGAN）通过在生成器和判别器中引入附加信息（标签）实现条件生成。基于互补学习的可控模型如Rumi-GAN被提出，利用负样本增强生成器对正样本的学习能力。本文评估了两种可控GAN变体——CGAN与Rumi-GAN——在生成满足特定约束（可玩性与可控性）的游戏关卡时的表现，分别在包含与不包含负样本的两种场景下进行。目的是检验引入负样本是否有助于避免生成不良输出。研究结果揭示了各方法在基于正负样本生成特定条件输出时的优势与局限。
原文摘要 · Abstract (English)
Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more controllable models. Conditional Generative Adversarial Networks (CGANs) extend vanilla GANs by conditioning both the generator and discriminator on some additional information (labels). Controllable models based on complementary learning, such as Rumi-GAN, have been introduced. Rumi-GANs leverage negative examples to enhance the generator's ability to learn positive examples. We evaluate the performance of two controllable GAN variants, CGAN and Rumi-GAN, in generating game levels targeting specific constraints of interest: playability and controllability. This evaluation is conducted under two scenarios: with and without the inclusion of negative examples. The goal is to determine whether incorporating negative examples helps the GAN models avoid generating undesirable outputs. Our findings highlight the strengths and weaknesses of each method in enforcing the generation of specific conditions when generating outputs based on given positive and negative examples.
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