AAAI Publications, Tenth Artificial Intelligence and Interactive Digital Entertainment Conference

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A Hierarchical Approach to Generating Maps Using Markov Chains
Sam Snodgrass, Santiago Ontanon

Last modified: 2014-09-05


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.


Procedural Content Generation; Artificial Intelligence; Markov Chains

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