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Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset by Zhang, Ruohan; Walshe, Calen; Liu, Zhuode; Guan, Lin; Muller, Karl S.; Whritner, Jake A.; Zhang, Luxin; Hayhoe, Mary M.; Ballard, Dana H. is a scholarly article available to read on EtoBox.

What is Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset about?

Large-scale public datasets have been shown to benefit research in multiple areas of modern artificial intelligence. For decision-making research that requires human data, high-quality datasets serve as important benchmarks to facilitate the development of new methods by providing a common reproducible standard. Many human decision-making tasks require visual attention to obtain high levels of performance. Therefore, measuring eye movements can provide a rich source of information about the strategies that humans use to solve decision-making tasks. Here, we provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. We demonstrate the usefulness of the dataset through two simple applications: predicting human gaze and imitating human demonstrated a

Author

Zhang, Ruohan; Walshe, Calen; Liu, Zhuode; Guan, Lin; Muller, Karl S.; Whritner, Jake A.; Zhang, Luxin; Hayhoe, Mary M.; Ballard, Dana H.

Published

2019

Language

EN