mlsanityData to 7 Oct 2026

The Impact of Visualizing Design Gradients for Human Designers

arXiv 2110.04147 ↗First posted 7 Oct 2021cs.HC cs.AIcited by 1

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Abstract (arXiv)

Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content. This area of research remains relatively under-explored, with the majority of mixed-initiative PCG level design systems using a common set of search-based PCG algorithms. In this paper, we introduce a mixed-initiative tool employing Exhaustive PCG (EPCG) for puzzle level design to further explore mixed-initiative PCG. We run an online human subject study in which individuals use the tool with an EPCG component turned on or off. Our analysis of the results demonstrates that, although a majority of users did not prefer the tool, it made the level design process significantly easier, and that the tool impacted the subjects' design process. This paper describes the study results and draws lessons for mixed-initiative PCG tool design.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 21 references that are in the map. First posted on arXiv 7 Oct 2021.

Builds on

5 of 21 references.

References · 5

  1. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 136
  2. 2019Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creatorscited by 31
  3. 2020Baba is Y'all: Collaborative Mixed-Initiative Level Designcited by 20
  4. 2018Co-Creative Level Design via Machine Learningcited by 18
  5. 2019Integrating Automated Play in Level Co-Creationcited by 1

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