The 2017 AIBIRDS Competition
This competition addresses the challenge of creating intelligent agents for physics-based puzzle games, which has applications to real-world problems, but is incremental as part of an ongoing series.
The paper describes the 2017 AIBIRDS competition, where participants developed agents to play Angry Birds, requiring physical reasoning and planning to solve unseen levels, with top agents achieving high scores and incorporating new techniques like deep reinforcement learning.
This paper presents an overview of the sixth AIBIRDS competition, held at the 26th International Joint Conference on Artificial Intelligence. This competition tasked participants with developing an intelligent agent which can play the physics-based puzzle game Angry Birds. This game uses a sophisticated physics engine that requires agents to reason and predict the outcome of actions with only limited environmental information. Agents entered into this competition were required to solve a wide assortment of previously unseen levels within a set time limit. The physical reasoning and planning required to solve these levels are very similar to those of many real-world problems. This year's competition featured some of the best agents developed so far and even included several new AI techniques such as deep reinforcement learning. Within this paper we describe the framework, rules, submitted agents and results for this competition. We also provide some background information on related work and other video game AI competitions, as well as discussing some potential ideas for future AIBIRDS competitions and agent improvements.