Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms

TL;DR

This paper introduces preference-learning emitters for mixed-initiative quality-diversity algorithms, enabling systems to learn user preferences and generate diverse, aligned suggestions more efficiently in creative tasks.

Contribution

It presents a novel framework for preference-learning emitters that adapt suggestions based on user preferences, improving alignment and efficiency in mixed-initiative co-creation.

Findings

Preference-learning emitters improve suggestion relevance.

Automated steps increase solutions per interaction.

User study validates effectiveness in game content generation.

Abstract

In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversity algorithms are commonly used for this purpose, as they can provide diverse suggestions that represent salient areas of the solution space, showcasing designs with high fitness and wide variety. Because generated suggestions drive the search process, it is important that they provide inspiration, but also stay aligned with the designer's intentions. Additionally, often many interactions with the system are required before the designer is content with a solution. In this work, we tackle these challenges with an interactive constrained MAP-Elites system that leverages emitters to learn the preferences of the designer and then use them in automated steps. By learning preferences, the generated designs remain aligned with the designer's intent, and by applying automatic steps, we generate more solutions per user interaction, giving a larger number of choices to the designer and thereby speeding up the search. We propose a general framework for preference-learning emitters (PLEs) and apply it to a procedural content generation task in the video game Space Engineers. We built an interactive application for our algorithm and performed a user study with players.