Generative Models for Procedural Content Generation in Complex Games

Abstract

Procedural content generation (PCG) has long been a crucial technique for creating vast and diverse game worlds, particularly in complex games where manual design would be infeasible. However, traditional PCG methods often struggle to produce content with the artistic quality, narrative coherence, and overall aesthetic appeal of content designed by human creators. This paper proposes a novel approach leveraging generative adversarial networks (GANs) to address these limitations. The core idea is to train a GAN to learn from a dataset of high-quality game design examples and subsequently generate new, novel content that adheres to the learned aesthetic and gameplay principles. We explore the potential of this approach, focusing on the underlying mathematical framework and the key components of the system. The generated content is evaluated based on metrics related to diversity, novelty, and adherence to design constraints. The proposed methodology offers a significant advancement over traditional rule-based PCG, moving towards a generative paradigm that can potentially unlock new levels of creativity and complexity in game development. The primary mathematical formulation centers around the adversarial training process, utilizing loss functions to guide the generator and discriminator towards a stable equilibrium. We outline the architecture of the GAN, detailing the generator network and the discriminator network, and explain the optimization techniques employed.