TY - JOUR AU - Snodgrass, Sam AU - Ontanon, Santiago PY - 2021/06/29 Y2 - 2024/03/28 TI - A Hierarchical Approach to Generating Maps Using Markov Chains JF - Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment JA - AIIDE VL - 10 IS - 1 SE - Full Oral Papers DO - 10.1609/aiide.v10i1.12708 UR - https://ojs.aaai.org/index.php/AIIDE/article/view/12708 SP - 59-65 AB - <p> 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. </p> ER -