# Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems

arXiv: https://arxiv.org/abs/2205.09269 · HTML: https://mlsanity.com/paper/2205.09269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 12 of 12)

- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)
- [Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks](https://mlsanity.com/paper/1603.01768/) (2016)
- [Creativity and Machine Learning: A Survey](https://mlsanity.com/paper/2104.02726/) (2021)
- [Baba is Y'all: Collaborative Mixed-Initiative Level Design](https://mlsanity.com/paper/2003.14294/) (2020)
- [Co-Creative Level Design via Machine Learning](https://mlsanity.com/paper/1809.09420/) (2018)
- [Dance Dance Convolution](https://mlsanity.com/paper/1703.06891/) (2017)
- [AffectGAN: Affect-Based Generative Art Driven by Semantics](https://mlsanity.com/paper/2109.14845/) (2021)
- [TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games](https://mlsanity.com/paper/2107.12506/) (2021)
- [GenerationMania: Learning to Semantically Choreograph](https://mlsanity.com/paper/1806.11170/) (2018)
- [Empathic AI Painter: A Computational Creativity System with Embodied Conversational Interaction](https://mlsanity.com/paper/2005.14223/) (2020)

## Built on by (most cited first, 1 of 1)

- [Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis](https://mlsanity.com/paper/2410.01096/) (2024)

## Abstract (arXiv)

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.