Angry Birds Level Generation to Achieve Diverse Results Having Controllable Similarity to an Example

• Published in: Journal of Information Processing Vol. 33; pp. 210 - 218
• Main Authors: Dewantoro, Mury F., Abdullah, Febri, Thawonmas, Ruck
• Format: Journal Article
• Language: English
• Published: Information Processing Society of Japan 2025; 一般社団法人 情報処理学会
• Subjects: angry birds; grammar-based methods; level generation; procedural content generation
• ISSN: 1882-6652; 1882-6652
• DOI: 10.2197/ipsjjip.33.210

This research proposes a procedural content generation (PCG) method for generating Angry Birds-like levels with controllable similarity to a given example. Ensuring the believability of generated levels is a key challenge in PCG. Using a believable example level, the challenge can be addressed by ensuring that a certain degree of similarity to the example is retained. Here, we focus on a method to control the degree of similarity. With controllable similarity, it is possible to adjust the resemblance of generated levels to an example level while maintaining diversity among the generated levels. To achieve this, our method partitions the example level into rows containing one or multiple blocks. Our method alters the said level by adding new blocks between rows. To control the similarity, we employ grammar-based rules extracted from the example to select the new blocks. Two evaluation metrics are employed: similarity score, which measures how well the generated levels retain similarity in a human perception, and diversity score, which measures the variety among generated structures. Our results suggest that the proposed method is capable of generating levels with controllable similarity to a given example while maintaining the diversity among the generated ones.