AIIDE Artifact Archive

Player Experience Extraction from Gameplay Video

Authors:
Date Archived: 13 November 2018
Accompanying Paper: Player Experience Extraction from Gameplay Video
Artifact URL: http://aiide.org/artifacts/files/peegv.zip
Alternative URL: https://github.com/soapisnotfat/experience-extraction-from-gameplay-video
Description:

The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This is a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. We present two approaches to derive game logs from game video via convolutional neural networks and transfer learning. We evaluatethe approaches in a Super Mario Bros. clone, Mega Man, and Skyrim. Our results demonstrate our approach outperforms random forest and other transfer baselines.

How to Cite This Artifact

The examples below show how to cite this page, but when referring to this work, many authors will prefer that you cite the accompanying paper linked above.

AAAI Style
Luo, Z. et al. 2018. Player Experience Extraction from Gameplay Video. http://aiide.org/artifacts/peegv. Accessed 23 November 2024.
IEEE Style
Z. Luo et al., "Player Experience Extraction from Gameplay Video," 2018. [Online]. Available: http://aiide.org/artifacts/peegv. [Accessed Nov 23, 2024].
BiBTeX Entry
@misc{peegv,
	author={Luo, Zijin and Guzdial, Matthew and Liao, Nicholas and Riedl, Mark},
	title={Player Experience Extraction from Gameplay Video},
	howpublished={\url{http://aiide.org/artifacts/peegv}},
	year={2018},
	note={Accessed 23 November 2024}
}