# Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds

arXiv: https://arxiv.org/abs/1902.02518 · HTML: https://mlsanity.com/paper/1902.02518/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution](https://mlsanity.com/paper/2104.06106/) (2021)
- [ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation](https://mlsanity.com/paper/2406.11875/) (2024)
- [Physics-Based Task Generation through Causal Sequence of Physical Interactions](https://mlsanity.com/paper/2308.02835/) (2023)

## Abstract (arXiv)

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.