# Evaluation of a Recommender System for Assisting Novice Game Designers

arXiv: https://arxiv.org/abs/1908.04629 · HTML: https://mlsanity.com/paper/1908.04629/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration](https://mlsanity.com/paper/2410.15644/) (2024)
- [Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules](https://mlsanity.com/paper/2309.09476/) (2023)
- [PANGeA: Procedural Artificial Narrative using Generative AI for Turn-Based Video Games](https://mlsanity.com/paper/2404.19721/) (2024)
- [Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis](https://mlsanity.com/paper/2410.01096/) (2024)
- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)

## Abstract (arXiv)

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improve developer workflow. We present a recommender system for assisting humans in game design as well as a rigorous human subjects study to validate it. The AI-driven game design assistance system suggests game mechanics to designers based on characteristics of the game being developed. We believe this method can bring creative insights and increase users' productivity. We conducted quantitative studies that showed the recommender system increases users' levels of accuracy and computational affect, and decreases their levels of workload.