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.
@article{arxiv.2308.13538,
title = {A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System},
author = {M Charity and Yash Bhartia and Daniel Zhang and Ahmed Khalifa and Julian Togelius},
journal= {arXiv preprint arXiv:2308.13538},
year = {2023}
}