Learning-Based Procedural Content Generation

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.