Evolutionary Level Repair

TL;DR

This paper presents a hybrid approach combining machine learning-based procedural content generation with search-based evolutionary algorithms to repair and improve non-functional game levels, ensuring they are playable and meet design constraints.

Contribution

It introduces a novel hybrid method that integrates PCGML with search-based repair algorithms to fix and enhance generated game levels.

Findings

Effective repair of broken game levels using evolutionary algorithms.

Successful integration of PCGML with search-based repair methods.

Promising results for hybrid procedural content generation in games.

Abstract

We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the completeness of the level, reachability of objects, or other performance characteristics. The repair problem may also be constrained in that it can only make a small number of changes to the level. We investigate search-based solutions to the level repair problem, particularly using evolutionary and quality-diversity algorithms, with good results. This level repair method is applied to levels generated using a machine learning-based procedural content generation (PCGML) method that generates stylistically appropriate but frequently broken levels. This combination of PCGML for generation and search-based methods for repair shows great promise as a hybrid procedural content generation (PCG) method.