A Hierarchical Approach to Generating Maps Using 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.