# The 2017 AIBIRDS Competition

arXiv: https://arxiv.org/abs/1803.05156 · HTML: https://mlsanity.com/paper/1803.05156/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Phy-Q as a measure for physical reasoning intelligence](https://mlsanity.com/paper/2108.13696/) (2021)
- [Deceptive Level Generation for Angry Birds](https://mlsanity.com/paper/2106.01639/) (2021)
- [Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI](https://mlsanity.com/paper/2510.04978/) (2025)
- [NovPhy: A Testbed for Physical Reasoning in Open-world Environments](https://mlsanity.com/paper/2303.01711/) (2023)
- [Physics-Based Task Generation through Causal Sequence of Physical Interactions](https://mlsanity.com/paper/2308.02835/) (2023)

## Abstract (arXiv)

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.