# Mortar: Evolving Mechanics for Automatic Game Design

arXiv: https://arxiv.org/abs/2601.00105 · HTML: https://mlsanity.com/paper/2601.00105/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 31 Dec 2025.

## Builds on (most cited first, 21 of 21)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Evolution through Large Models](https://mlsanity.com/paper/2206.08896/) (2022)
- [LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization](https://mlsanity.com/paper/2306.01102/) (2023)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [MarioGPT: Open-Ended Text2Level Generation through Large Language Models](https://mlsanity.com/paper/2302.05981/) (2023)
- [Increasing Generality in Machine Learning through Procedural Content Generation](https://mlsanity.com/paper/1911.13071/) (2019)
- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [Practical PCG Through Large Language Models](https://mlsanity.com/paper/2305.18243/) (2023)
- [Word2World: Generating Stories and Worlds through Large Language Models](https://mlsanity.com/paper/2405.06686/) (2024)
- [General Video Game Rule Generation](https://mlsanity.com/paper/1906.05160/) (2019)
- [A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking](https://mlsanity.com/paper/1809.02904/) (2018)
- [Automatic Game Design via Mechanic Generation](https://mlsanity.com/paper/1908.01420/) (2019)
- [Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules](https://mlsanity.com/paper/2309.09476/) (2023)
- [Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis](https://mlsanity.com/paper/2410.01096/) (2024)
- [Word2Minecraft: Generating 3D Game Levels through Large Language Models](https://mlsanity.com/paper/2503.16536/) (2025)
- [Evolving Game Skill-Depth using General Video Game AI Agents](https://mlsanity.com/paper/1703.06275/) (2017)
- [ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search](https://mlsanity.com/paper/2506.06524/) (2025)
- [Automatic Critical Mechanic Discovery Using Playtraces in Video Games](https://mlsanity.com/paper/1909.03094/) (2019)

## Built on by (most cited first, 1 of 1)

- [Programmable Cellular Automata](https://mlsanity.com/paper/2609.06102/) (2026)

## Abstract (arXiv)

We present Mortar, a system for autonomously evolving game mechanics for automatic game design. Game mechanics define the rules and interactions that govern gameplay, and designing them manually is a time-consuming and expert-driven process. Mortar combines a quality-diversity algorithm with a large language model to explore a diverse set of mechanics, which are evaluated by synthesising complete games that incorporate both evolved mechanics and those drawn from an archive. The mechanics are evaluated by composing complete games through a tree search procedure, where the resulting games are evaluated by their ability to preserve a skill-based ordering over players -- that is, whether stronger players consistently outperform weaker ones. We assess the mechanics based on their contribution towards the skill-based ordering score in the game. We demonstrate that Mortar produces games that appear diverse and playable, and mechanics that contribute more towards the skill-based ordering score in the game. We perform ablation studies to assess the role of each system component and a user study to evaluate the games based on human feedback.