  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.
