Ensemble Learning For Mega Man Level Generation

• URL: http://arxiv.org/abs/2107.12524v1
• Date: Tue, 27 Jul 2021 00:16:23 GMT
• Title: Ensemble Learning For Mega Man Level Generation
• Authors: Bowei Li, Ruohan Chen, Yuqing Xue, Ricky Wang, Wenwen Li, and Matthew Guzdial
• Abstract summary: We investigate the use of ensembles of Markov chains for procedurally generating emphMega Man levels. We evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.
• Score: 2.6402344419230697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying data with a single model. In this paper, we investigated the use of ensembles of Markov chains for procedurally generating \emph{Mega Man} levels. We conduct an initial investigation of our approach and evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.

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