{"type":"ScholarlyArticle","title":"Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems","authors":[{"name":"Emily Halina","slug":"emily-halina","given":"Emily","family":"Halina","affiliation":"Department of Computing Science , Alberta Machine Intelligence Institute (Amii) University of Alberta , Edmonton , Alberta , Canada"},{"name":"Matthew Guzdial","slug":"matthew-guzdial","given":"Matthew","family":"Guzdial","affiliation":"Department of Computing Science , Alberta Machine Intelligence Institute (Amii) University of Alberta , Edmonton , Alberta , Canada"}],"abstract":"To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system to a co-designer. In this paper we present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designer. We evaluate our approach with a human subject study using a co-creative rhythm game design tool. We find that designers prefer our proposed method and produce higher quality content in comparison to an existing baseline.","doi":"10.24963/ijcai.2022/685","url":"https://ijcai.org/proceedings/2022/685","pdf":"https://ijcai.org/proceedings/2022/0685.pdf","markdown":"https://ijcai.org/proceedings/2022/685.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/685","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI and Arts","number":685,"pages":{"first":4943,"last":4949},"keywords":["Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Theory and philosophy of arts and creativity in AI systems: Support of human creativity","Application domains: Games"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987168","length_sec":82,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985311","length_sec":694,"provider":"slideslive"}],"references":[{"position":1,"raw":"[5argon, 2018] 5argon. 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