Game Level Generation from Gameplay Video
Algorithms that generate computer game content require game design knowledge. We present an approach to automatically learn game design knowledge for level design from gameplay videos. We further demonstrate how the acquired design knowledge can be used to generate sections of game levels. Our approach involves parsing video of people playing a game to detect the appearance of patterns of sprites and utilizing machine learning to build a probabilistic model of sprite placement. We show how rich game design information can be automatically parsed from gameplay videos and represented as a set of generative probabilistic models. We use Super Mario Bros. as a proof of concept. We evaluate our approach on a measure of playability and stylistic similarity to the original levels as represented in the gameplay videos.Citation
M. Guzdial, M. Riedl. "Game Level Generation from Gameplay Video". Artificial Intelligence and Interactive Entertainment Conference (AIIDE), (ed: Nathan Sturtevant, Brian Magerko), pp 44-50, October 2016.| Keywords: | |
| Category: | In Conference |
| Web Links: | AAAI |
BibTeX
@incollection{Guzdial+Riedl:AIIDE16,
author = {Matthew Guzdial and Mark Riedl},
title = {Game Level Generation from Gameplay Video},
Editor = {Nathan Sturtevant, Brian Magerko},
Pages = {44-50},
booktitle = {Artificial Intelligence and Interactive Entertainment Conference
(AIIDE)},
year = 2016,
}Last Updated: October 29, 2020Submitted by Sabina P
