Ensemble Learning For Mega Man Level Generation

TL;DR

This paper explores using ensemble Markov chains to improve procedural generation of Mega Man levels, aiming to better capture data variance and enhance style and playability.

Contribution

It introduces an ensemble approach to Markov chain-based level generation, addressing limitations of single models in capturing data variance.

Findings

Ensemble method improves level playability.

Ensemble approach enhances stylistic similarity.

Compared to single Markov chain, the ensemble yields more diverse levels.

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.