A Hierarchical MdMC Approach to 2D Video Game Map Generation

Authors

  • Sam Snodgrass Drexel University
  • Santiago Ontanon Drexel University

DOI:

https://doi.org/10.1609/aiide.v11i1.12794

Keywords:

Artificial Intelligence, Markov Chains, Procedural Content Generation, Games

Abstract

In this paper we describe a hierarchical method for procedurally generating 2D game maps using multi-dimensional Markov chains (MdMCs). Our method takes a collection of 2D game maps, breaks them into small chunks and performs clustering to find a set of chunks that correspond to high-level structures (high-level tiles) in the training maps. This set of high-level tiles is then used to re-represent the training maps, and to fit two sets of MdMC models: a high-level model captures the distribution of high-level tiles in the map, and a set of low-level models capture the internal structure of each high-level tile. These two sets of models can then be used to hierarchically generate new maps. We test our approach using two classic games, Super Mario Bros. and Loderunner, and compare the results against other existing map generators.

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Published

2021-06-24

How to Cite

Snodgrass, S., & Ontanon, S. (2021). A Hierarchical MdMC Approach to 2D Video Game Map Generation. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 11(1), 205-211. https://doi.org/10.1609/aiide.v11i1.12794