@inproceedings{23b1d6111a14413eb01916e64bf5e778,
title = "Linear levels through n-grams",
abstract = "We show that novel, linear game levels can be created using ngrams that have been trained on a corpus of existing levels. The method is fast and simple, and produces levels that are recognisably in the same style as those in the corpus that it has been trained on. We use Super Mario Bros. as an example domain, and use a selection of the levels from the original game as a training corpus. We treat Mario levels as a left-to-right sequence of vertical level slices, allowing us to perform level generation in a setting with some formal similarities to n-gram-based text generation and music generation. In empirical results, we investigate the effects of corpus size and n (sequence length). While the applicability of the method might seem limited to the relatively narrow domain of 2D games, we argue that many games in effect have linear levels and n-grams could be used to good effect, given that a suitable alphabet can be found. Copyright is held by the owner/author(s). Publication rights licensed to ACM.",
keywords = "N-grams, Procedural content generation, Videogames",
author = "Steve Dahlskog and Julian Togelius and Nelson, {Mark J.}",
year = "2014",
month = nov,
day = "4",
doi = "10.1145/2676467.2676506",
language = "English (US)",
series = "MINDTREK 2014 - Proceedings of the 18th International Academic MindTrek Conference:",
publisher = "Association for Computing Machinery, Inc",
pages = "200--206",
editor = "Helja Franssila and Janne Paavilainen and Artur Lugmayr",
booktitle = "MINDTREK 2014 - Proceedings of the 18th International Academic MindTrek Conference",
note = "18th International Academic MindTrek Conference, MINDTREK 2014 ; Conference date: 04-11-2014 Through 06-11-2014",
}