From Unstable to Playable: Stabilizing Angry Birds Levels via Object Segmentation
This addresses the challenge of ensuring high-quality, industry-standard content in PCG for game developers, though it is incremental as it builds on existing PCG approaches.
The researchers tackled the problem of unstable levels generated by Procedural Content Generation (PCG) models in games, specifically using Angry Birds as a case study, and their method improved stability and playability through object segmentation and targeted repairs.
Procedural Content Generation (PCG) techniques enable automatic creation of diverse and complex environments. While PCG facilitates more efficient content creation, ensuring consistently high-quality, industry-standard content remains a significant challenge. In this research, we propose a method to identify and repair unstable levels generated by existing PCG models. We use Angry Birds as a case study, demonstrating our method on game levels produced by established PCG approaches. Our method leverages object segmentation and visual analysis of level images to detect structural gaps and perform targeted repairs. We evaluate multiple object segmentation models and select the most effective one as the basis for our repair pipeline. Experimental results show that our method improves the stability and playability of AI-generated levels. Although our evaluation is specific to Angry Birds, our image-based approach is designed to be applicable to a wide range of 2D games with similar level structures.