Cognitive Geometry for Procedural Content Generation

Abstract

This paper explores the application of cognitive geometry to procedural content generation. We propose a system that learns and generates levels, landscapes, and other procedural elements by mapping visual representations to underlying cognitive processes. The core mechanism involves training a neural network to translate human cognitive processes - specifically spatial reasoning and pattern recognition - into a geometry-based representation. This approach aims to move beyond rigid rules and towards a more adaptive and intuitive generation process, offering a novel framework for creating dynamic and engaging game worlds and environments. The paper discusses the system's architecture, training methodology, and preliminary results demonstrating the system's ability to produce diverse and consistent procedural content.