Procedural generation of angry birds levels with adjustable difficulty
Misaki Kaidan, Tomohiro Harada, Chun Yin Chu, Ruck Thawonmas · 2016
This paper proposes a procedural generation algorithm that creates game levels for the Angry Birds game. Game levels are automatically generated using a genetic algorithm (GA), and their difficulty is adjusted by a parameter introduced in the fitness function of GA. In addition, the Extended Rectangle Algebra (ERA) is used in order to analyze these levels. By calculating ERA relations between objects, effects of a bird hitting on an object are quantified by the aforementioned parameter. Our experiment proves that this parameter has a strong correlation of 96% with the players' winning percentage. Accordingly, it shows that any difficulty level can be generated by regulating the aforementioned parameter.