Learning-Based Procedural Content Generation

TL;DR

This paper introduces a learning-based framework for procedural content generation in games, leveraging data-driven methods to create adaptable, high-quality game content with minimal disruption to players.

Contribution

It proposes a novel, generic learning-based PCG framework that addresses limitations of search-based methods by utilizing game development and beta test data.

Findings

Framework can generate adaptable game content.

Prototype successfully applied to Quake.

Simulation shows promising quality of generated content.

Abstract

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game researches. Among a variety of PCG techniques, search-based approaches overwhelmingly dominate PCG development at present. While SBPCG leads to promising results and successful applications, it poses a number of challenges ranging from representation to evaluation of the content being generated. In this paper, we present an alternative yet generic PCG framework, named learning-based procedure content generation (LBPCG), to provide potential solutions to several challenging problems in existing PCG techniques. By exploring and exploiting information gained in game development and public beta test via data-driven learning, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their experience. Furthermore, we develop 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.