# A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System

arXiv: https://arxiv.org/abs/2308.13538 · HTML: https://mlsanity.com/paper/2308.13538/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 16 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [General Video Game Rule Generation](https://mlsanity.com/paper/1906.05160/) (2019)
- [Pitako -- Recommending Game Design Elements in Cicero](https://mlsanity.com/paper/1907.03877/) (2019)

## Built on by (most cited first, 6 of 6)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Large Language Models and Video Games: A Preliminary Scoping Review](https://mlsanity.com/paper/2403.02613/) (2024)
- [The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game Designer](https://mlsanity.com/paper/2403.02454/) (2024)
- [GPT for Games: A Scoping Review (2020-2023)](https://mlsanity.com/paper/2404.17794/) (2024)
- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [Cardiverse: Harnessing LLMs for Novel Card Game Prototyping](https://mlsanity.com/paper/2502.07128/) (2025)

## Abstract (arXiv)

This paper introduces a system used to generate game feature suggestions based on a text prompt. Trained on the game descriptions of almost 60k games, it uses the word embeddings of a small GLoVe model to extract features and entities found in thematically similar games which are then passed through a generator model to generate new features for a user's prompt. We perform a short user study comparing the features generated from a fine-tuned GPT-2 model, a model using the ConceptNet, and human-authored game features. Although human suggestions won the overall majority of votes, the GPT-2 model outperformed the human suggestions in certain games. This system is part of a larger game design assistant tool that is able to collaborate with users at a conceptual level.