Angry Birds as a Challenge for Artificial Intelligence
Authors: Jochen Renz, XiaoYu Ge, Rohan Verma, Peng Zhang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The Angry Birds AI Competition has been held annually since 2012... We also summarise some highlights of past competitions, including a new competition track we introduced recently. After each AI competition, we hold a Man vs Machine Challenge to test if AI agents are already better than humans. In previous competitions, humans always won with a wide, but shrinking margin. In 2013, half of human participants were better than the best AI, while in 2014 it was a third. |
| Researcher Affiliation | Academia | Jochen Renz Xiao Yu Ge, Rohan Verma, Peng Zhang Research School of Computer Science The Australian National University jochen.renz@anu.edu.au |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions that 'Starting in 2014, the best teams have made the source code of their agents available' for the competition, but it does not provide open-source code for any methodology presented by the authors of this paper. |
| Open Datasets | No | The paper refers to 'Angry Birds levels' as the basis for the competition, but it does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset of these levels for research purposes. |
| Dataset Splits | No | The paper describes a competition involving 'new Angry Birds levels' but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions 'Box2D' as a game internal physics engine, but it does not provide specific ancillary software details with version numbers for any experimental setup. |
| Experiment Setup | No | The paper describes the competition and various AI approaches, but it does not provide specific experimental setup details such as hyperparameter values or training configurations. |