Ensemble Learning For Mega Man Level Generation
This work addresses the problem of generating diverse and playable game levels for Mega Man, but it is incremental as it builds on existing Markov chain methods.
The paper tackled the challenge of capturing variance in procedural content generation for Mega Man levels by using ensembles of Markov chains, resulting in improved playability and stylistic similarity compared to a non-ensemble approach.
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.