Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds
This addresses personalized gaming experiences for players of physics-based puzzle games, but it is incremental as it builds on a pre-existing level generator.
The paper tackles dynamic difficulty adjustment in Angry Birds by proposing an adaptive level generation algorithm that tailors levels to player performance, evaluated using AI agents to optimize level properties efficiently.
This paper presents an adaptive level generation algorithm for the physics-based puzzle game Angry Birds. The proposed algorithm is based on a pre-existing level generator for this game, but where the difficulty of the generated levels can be adjusted based on the player's performance. This allows for the creation of personalised levels tailored specifically to the player's own abilities. The effectiveness of our proposed method is evaluated using several agents with differing strategies and AI techniques. By using these agents as models / representations of real human player's characteristics, we can optimise level properties efficiently over a large number of generations. As a secondary investigation, we also demonstrate that by combining the performance of several agents together it is possible to generate levels that are especially challenging for certain players but not others.