# The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games

arXiv: https://arxiv.org/abs/2311.04710 · HTML: https://mlsanity.com/paper/2311.04710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 163 references that are in the map.
- First posted on arXiv 8 Nov 2023.

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

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network](https://mlsanity.com/paper/2007.05674/) (2020)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [Baba is Y'all: Collaborative Mixed-Initiative Level Design](https://mlsanity.com/paper/2003.14294/) (2020)
- [Mech-Elites: Illuminating the Mechanic Space of GVGAI](https://mlsanity.com/paper/2002.04733/) (2020)
- [Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions](https://mlsanity.com/paper/2003.03377/) (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)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study](https://mlsanity.com/paper/2005.07478/) (2020)
- [Evolutionary Search in the Space of Rules for Creation of New Two-Player Board Games](https://mlsanity.com/paper/1406.0175/) (2014)
- [Procedural Content Generation for GDL Descriptions of Simplified Boardgames](https://mlsanity.com/paper/1508.00212/) (2015)
- [(Genetically) Improving Novelty in Procedural Story Generation](https://mlsanity.com/paper/2103.06935/) (2021)
- [Illuminating the Space of Enemies Through MAP-Elites](https://mlsanity.com/paper/2202.09615/) (2022)
- [Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials](https://mlsanity.com/paper/2204.05217/) (2022)

## Abstract (arXiv)

Video games demand is constantly increasing, which requires the costly production of large amounts of content. Towards this challenge, researchers have developed Search-Based Procedural Content Generation (SBPCG), that is, the (semi-)automated creation of content through search algorithms. We survey the current state of SBPCG, reporting work appeared in the field between 2011-2022 and identifying open research challenges. The results lead to recommendations for practitioners and to the identification of several potential future research avenues for SBPCG.