Deceptive Level Generation for Angry Birds

TL;DR

This paper introduces an automated method to generate deceptive levels for Angry Birds, aiming to challenge AI agents by creating levels that mimic human deception and fool current algorithms.

Contribution

It presents a novel procedure for generating deceptive levels across six categories, filling a gap in existing content generators that lack deception focus.

Findings

Generated levels mimic human deceptive characteristics

Metrics for stability, solvability, and deception degree are defined

Levels successfully fool state-of-the-art AI agents

Abstract

The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many different agents with various approaches have been employed over the competition's lifetime to solve this task. Even though the performance of these agents has increased significantly over the past few years, they still show major drawbacks in playing deceptive levels. This is because most of the current agents try to identify the best next shot rather than planning an effective sequence of shots. In order to encourage advancements in such agents, we present an automated methodology to generate deceptive game levels for Angry Birds. Even though there are many existing content generators for Angry Birds, they do not focus on generating deceptive levels. In this paper, we propose a procedure to generate deceptive levels for six deception categories that can fool the state-of-the-art Angry Birds playing AI agents. Our results show that generated deceptive levels exhibit similar characteristics of human-created deceptive levels. Additionally, we define metrics to measure the stability, solvability, and degree of deception of the generated levels.