Learning player tailored content from observation: Platformer level generation from video traces using lstms
A Summerville, M Guzdial, M Mateas…�- Proceedings of the AAAI�…, 2016 - ojs.aaai.org
Proceedings of the AAAI Conference on Artificial Intelligence and�…, 2016•ojs.aaai.org
A touted use of Procedural Content Generation is generating content tailored to specific
players. Previous work has relied on human identification of player profile features which are
then mapped to level generator features. We present a machine-learned technique to train
generators on Super Mario Bros. videos, generating levels based on latent play styles
learned from the video. We evaluate the generators in comparison to the original levels and
a machine-learned generator trained using simulated players.
players. Previous work has relied on human identification of player profile features which are
then mapped to level generator features. We present a machine-learned technique to train
generators on Super Mario Bros. videos, generating levels based on latent play styles
learned from the video. We evaluate the generators in comparison to the original levels and
a machine-learned generator trained using simulated players.
Abstract
A touted use of Procedural Content Generation is generating content tailored to specific players. Previous work has relied on human identification of player profile features which are then mapped to level generator features. We present a machine-learned technique to train generators on Super Mario Bros. videos, generating levels based on latent play styles learned from the video. We evaluate the generators in comparison to the original levels and a machine-learned generator trained using simulated players.
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