Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 780-785.
Abstract
Statistical models, such as Markov chains, have recently started to be studied for the purpose of Procedural Content Generation (PCG). A major problem with this approach is controlling the sampling process in order to obtain output satisfying some desired constraints. In this paper we present three approaches to constraining the content generated using multi-dimensional Markov chains: (1) a generate and test approach that simply resamples the content until the desired constraints are satisfied, (2) an approach that finds and resamples parts of the generated content that violate the constraints, and (3) an incremental method that checks for constraint violations during sampling. We test our approaches by generating maps for two classic video games, Super Mario Bros. and Kid Icarus.
@inproceedings{ijcai2016p116,
title = {Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling},
author = {Snodgrass, Sam and Ontañón, Santiago},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {780--785},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/116},
}
TY - CPAPER TI - Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling AU - Snodgrass, Sam AU - Ontañón, Santiago T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 780 EP - 785 UR - https://ijcai.org/proceedings/2016/116 ER -Copied
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