Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Science

Background:

• Variational Autoencoders (VAEs) are established for image generation and dimensionality reduction.
• Their application in game design and data clustering is underexplored.
• Modern applications often combine VAEs with other machine learning frameworks.

Purpose of the Study:

• To investigate the mathematical properties of VAEs, including encoding/decoding and loss functions.
• To apply VAEs for generating novel game levels in established game settings.
• To validate VAEs' data clustering capabilities using the MNIST database.

Main Methods:

• Theoretical analysis of VAE mathematical properties.
• Implementation of VAE for procedural content generation in games.
• Empirical evaluation using statistical metrics and the MNIST dataset.
• Comparative assessment of VAE performance in distinct case studies.

Main Results:

• The VAE model demonstrated potential in generating game levels and clustering data.
• Statistical metrics confirmed the model's performance in the case studies.
• Identified limitations include handling high-dimensional data and output clarity.

Conclusions:

• VAEs show promise for game design and data clustering, but require further development.
• Future enhancements like tokenization and VAE-GAN integration are proposed.
• Optimizing VAEs can enhance their utility in game development and industrial applications.