A Hierarchical Approach to Generating Maps Using Markov Chains


  • Sam Snodgrass Drexel University
  • Santiago Ontanon Drexel University




Procedural Content Generation, Artificial Intelligence, Markov Chains


In this paper we describe a hierarchical method for procedurallygenerating maps using Markov chains. Ourmethod takes as input a collection of human-authoredtwo-dimensional maps, and splits them into high-leveltiles which capture large structures. Markov chains arethen learned from those maps to capture the structure ofboth the high-level tiles, as well as the low-level tiles.Then, the learned Markov chains are used to generatenew maps by first generating the high-level structure ofthe map using high-level tiles, and then generating thelow-level layout of the map. We validate our approachusing the game Super Mario Bros., by evaluating thequality of maps produced using different configurationsfor training and generation.




How to Cite

Snodgrass, S., & Ontanon, S. (2021). A Hierarchical Approach to Generating Maps Using Markov Chains. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 10(1), 59-65. https://doi.org/10.1609/aiide.v10i1.12708