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

05/19/2022
by   Emily Halina, et al.
0

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.

READ FULL TEXT
research
07/25/2020

Towards Game Design via Creative Machine Learning (GDCML)

In recent years, machine learning (ML) systems have been increasingly ap...
research
09/11/2023

Online ML Self-adaptation in Face of Traps

Online machine learning (ML) is often used in self-adaptive systems to s...
research
03/20/2018

The Three Pillars of Machine Programming

In this position paper, we describe our vision of the future of machine ...
research
06/17/2020

Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders

Procedural content generation via machine learning (PCGML) has demonstra...
research
03/19/2021

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Two established approaches to engineer adaptive systems are architecture...
research
10/18/2022

A Human-ML Collaboration Framework for Improving Video Content Reviews

We deal with the problem of localized in-video taxonomic human annotatio...
research
01/24/2019

SAM: A Modular Framework for Self-Adapting Web Menus

This paper presents SAM, a modular and extensible JavaScript framework f...

Please sign up or login with your details

Forgot password? Click here to reset