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

TL;DR

This paper introduces an adaptive level generation algorithm for Angry Birds that personalizes difficulty based on player performance, using AI agents to optimize and tailor levels for individual players.

Contribution

It presents a novel adaptive level generator that personalizes difficulty in Angry Birds by modeling player performance with AI agents, enabling tailored and challenging levels.

Findings

Levels can be effectively personalized to player abilities.

Combining multiple agent performances creates levels challenging for specific players.

The method demonstrates efficient optimization over many generations.

Abstract

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.