{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intentional-computational-level-design","title":"Intentional Computational Level Design","arxiv_id":"1904.08972","date":"2019-04-18","proceeding":null,"authors":["Ahmed Khalifa","Michael Cerny Green","Gabriella Barros","Julian Togelius"],"abstract":"The procedural generation of levels and content in video games is a\nchallenging AI problem. Often such generation relies on an intelligent way of\nevaluating the content being generated so that constraints are satisfied and/or\nobjectives maximized. In this work, we address the problem of creating levels\nthat are not only playable but also revolve around specific mechanics in the\ngame. We use constrained evolutionary algorithms and quality-diversity\nalgorithms to generate small sections of Super Mario Bros levels called scenes,\nusing three different simulation approaches: Limited Agents, Punishing Model,\nand Mechanics Dimensions. All three approaches are able to create scenes that\ngive opportunity for a player to encounter or use targeted mechanics with\ndifferent properties. We conclude by discussing the advantages and\ndisadvantages of each approach and compare them to each other.","url_abs":"http://arxiv.org/abs/1904.08972v1","url_pdf":"http://arxiv.org/pdf/1904.08972v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"intentional-computational-level-design","repo_url":"https://github.com/amidos2006/Mario-AI-Framework","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}