# Generating Levels That Teach Mechanics

arXiv: https://arxiv.org/abs/1807.06734 · HTML: https://mlsanity.com/paper/1807.06734/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [10 Years of the PCG workshop: Past and Future Trends](https://mlsanity.com/paper/2104.11037/) (2021)
- [Automatic Generation of Board Game Manuals](https://mlsanity.com/paper/2109.09507/) (2021)
- [Predicting Personas Using Mechanic Frequencies and Game State Traces](https://mlsanity.com/paper/2203.13351/) (2022)
- [Generating Game Levels of Diverse Behaviour Engagement](https://mlsanity.com/paper/2207.02100/) (2022)
- [Game Mechanic Alignment Theory and Discovery](https://mlsanity.com/paper/2102.10247/) (2021)
- [Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials](https://mlsanity.com/paper/2204.05217/) (2022)
- [Level Assembly as a Markov Decision Process](https://mlsanity.com/paper/2304.13922/) (2023)
- [The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games](https://mlsanity.com/paper/2311.04710/) (2023)

## Abstract (arXiv)

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.