A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System
This is an incremental step for game designers, offering a conceptual assistant tool to aid in feature generation.
The paper tackles generating game feature suggestions from text prompts by training on 60k game descriptions and using GLoVe embeddings and a generator model, with a user study showing that a fine-tuned GPT-2 model outperformed human suggestions in some games.
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.