2023/08/16 by M Charity, Charity, M, Yash Bhartia +7
Computer Science · Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Computation and Language (cs.CL) #Digital Games and Media #Educational Games and Gamification #FOS: Computer and information sciences #Information Retrieval (cs.IR)
paper · pdf · doi:10.48550/arxiv.2308.13538
openalex publication_date 2023/08/16 · openalex created_date 2023/08/31 · openalex updated_date 2026/07/28
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.