@inproceedings{cbeffbbd64ca4b81b213de513b4603b2,
title = "Lode Encoder: AI-constrained co-creativity",
abstract = "We present Lode Encoder, a gamified mixed-initiative level creation system for the classic platform-puzzle game Lode Runner. The system is built around several autoen-coders which are trained on sets of Lode Runner levels. When fed with the user's design, each autoencoder produces a version of that design which is closer in style to the levels that it was trained on. The Lode Encoder interface allows the user to build and edit levels through 'painting' from the suggestions provided by the autoencoders. Crucially, in order to encourage designers to explore new possibilities, the system does not include more traditional editing tools. We report on the system design and training procedure, as well as on the evolution of the system itself and user tests.",
keywords = "Co-Creation, Level Design, Machine Learning, Mixed Initiative, Variational Autoencoders",
author = "Debosmita Bhaumik and Ahmed Khalifa and Julian Togelius",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Conference on Games, CoG 2021 ; Conference date: 17-08-2021 Through 20-08-2021",
year = "2021",
doi = "10.1109/CoG52621.2021.9619009",
language = "English (US)",
series = "IEEE Conference on Computatonal Intelligence and Games, CIG",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE Conference on Games, CoG 2021",
}