# Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials

arXiv: https://arxiv.org/abs/2204.05217 · HTML: https://mlsanity.com/paper/2204.05217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics](https://mlsanity.com/paper/1802.06881/) (2018)
- [Talakat: Bullet Hell Generation through Constrained Map-Elites](https://mlsanity.com/paper/1806.04718/) (2018)
- [Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding](https://mlsanity.com/paper/2112.03534/) (2021)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites](https://mlsanity.com/paper/1906.05175/) (2019)
- [Mech-Elites: Illuminating the Mechanic Space of GVGAI](https://mlsanity.com/paper/2002.04733/) (2020)
- [Generating Levels That Teach Mechanics](https://mlsanity.com/paper/1807.06734/) (2018)
- [Intentional Computational Level Design](https://mlsanity.com/paper/1904.08972/) (2019)
- [Mario Level Generation From Mechanics Using Scene Stitching](https://mlsanity.com/paper/2002.02992/) (2020)
- [Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI](https://mlsanity.com/paper/2008.11258/) (2020)
- [Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)](https://mlsanity.com/paper/2110.04146/) (2021)
- [AtDelfi: Automatically Designing Legible, Full Instructions For Games](https://mlsanity.com/paper/1807.04375/) (2018)
- [Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation](https://mlsanity.com/paper/2108.08762/) (2021)
- [Predicting Personas Using Mechanic Frequencies and Game State Traces](https://mlsanity.com/paper/2203.13351/) (2022)

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

- [The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games](https://mlsanity.com/paper/2311.04710/) (2023)

## Abstract (arXiv)

In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover different rules and game mechanics. Procedural personas can guide generators to create content which encourages or discourages certain playstyle behaviors. In this system, we use procedural personas to calculate the behavioral characteristics of levels which are evolved using the quality-diversity algorithm known as Constrained MAP-Elites. An evolved map's quality is determined by its simplicity: the simpler it is, the better it is. Within this work, we show that the generated maps can strongly encourage or discourage different persona-like behaviors and range from simple solutions to complex puzzle-levels, making them perfect candidates for a tutorial generative system.