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