HackerPapers

arXiv:2403.02454cs.AI

The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designer

Asad Anjum, Yuting Li, Noelle Law, M Charity, Julian Togelius

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

This paper studies how large language models (LLMs) can act as effective, high-level creative collaborators and ``muses'' for game design. We model the design of this study after the exercises artists use by looking at amorphous ink splotches for creative inspiration. Our goal is to determine whether AI-assistance can improve, hinder, or provide an alternative quality to games when compared to the creative intents implemented by human designers. The capabilities of LLMs as game designers are stress tested by placing it at the forefront of the decision making process. Three prototype games are designed across 3 different genres: (1) a minimalist base game, (2) a game with features and game feel elements added by a human game designer, and (3) a game with features and feel elements directly implemented from prompted outputs of the LLM, ChatGPT. A user study was conducted and participants were asked to blindly evaluate the quality and their preference of these games. We discuss both the development process of communicating creative intent to an AI chatbot and the synthesized open feedback of the participants. We use this data to determine both the benefits and shortcomings of AI in a more design-centric role.

arXiv comments
12 pages
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Mar 2024 to Mar 2024. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.