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Generative adversarial networks (GANs) can create personalized game levels matching player skill. ResNet-GAN and U-Net GAN architectures offer the best balance for adaptive procedural content generation.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Game Development

Background:

  • Procedural content generation (PCG) faces challenges in matching game level difficulty to player skill, impacting engagement.
  • Deep generative models, particularly Generative Adversarial Networks (GANs), offer potential for automated, scalable level synthesis.
  • The effectiveness of different GAN architectures for skill-conditioned and personalized level generation requires further investigation.

Purpose of the Study:

  • To investigate and compare the effectiveness of five GAN architectures for skill-conditioned procedural content generation.
  • To evaluate GANs' ability to generate personalized game levels tailored to individual player skill levels.
  • To identify optimal GAN architectures for balancing level diversity, playability, and training stability in adaptive PCG.

Main Methods:

  • Utilized five GAN architectures: U-Net GAN, StyleGAN, Deep Convolutional GAN (DCGAN), ResNet-GAN, and Spectral Normalization GAN (SN-GAN).
  • Employed Spectral Clustering to group player behavior into distinct skill levels, using these labels as conditioning signals for GANs.
  • Evaluated models using quantitative metrics: tile distribution entropy, diversity score, discriminator accuracy, generation speed, and pairwise Hamming distance.

Main Results:

  • Generated levels adapted to increasing player skill, showing fewer deaths and shorter completion times.
  • Higher player skill levels encouraged more complex in-game interactions.
  • ResNet-based and U-Net-style GANs demonstrated the best performance regarding diversity, playability, and training stability.
  • Spectral clustering effectively enabled skill-conditioning for personalized level generation.

Conclusions:

  • GANs, particularly ResNet and U-Net variants, are effective for skill-conditioned procedural content generation.
  • The proposed spectral clustering approach successfully generates personalized game levels aligned with player abilities.
  • This research supports the advancement of adaptive procedural content generation systems for enhanced player engagement.