Deceptive angry birds

Abstract

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been proposed to solve the complex and challenging physical reasoning problems associated with such a game. The performance of these agents has increased significantly over the competition's lifetime thanks to the different approaches and improved techniques employed. However, there still exist key flaws within the designs of these agents that can often lead them to make illogical or very poor choices. Most of the current approaches try to identify the best or a good next shot, but do not attempt to plan an effective sequence of shots. While this might be due to the difficulty in predicting the exact outcome of a shot, this capability is precisely what is needed to succeed, both in games like Angry Birds, but also in the real world where physical reasoning capabilities are essential. In order to encourage development of such techniques, we can create levels where selecting a seemingly good next shot will lead to a worse outcome. In this paper we present several categories of deception to fool the current state-of-the-art agents. By evaluating the performance of the most recent Angry Birds agents on specific level examples that contain these deceptive elements, we can show how certain AI techniques can be tricked or exploited. We also propose some ways that future agents could help deal with these deceptive levels to increase their overall performance and generality.