---
title: "General Statistical Approaches to Procedural Map Generation"
authors:
  - "Sam Snodgrass"
doi: ""
url: "https://ijcai.org/proceedings/2016/588"
pdf: "https://ijcai.org/proceedings/2016/0588.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4028-4028"
track: ""
keywords:
---

# General Statistical Approaches to Procedural Map Generation

Sam Snodgrass

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 4028-4028.

## Abstract

Procedural content generation (PCG) studies the algorithmic creation of content (e.g., textures, maps), typically for games. PCG has become a popular research topic in recent years, but little has been done in terms of generalized content generators: approaches that can generate content for a variety of games without hand-tuning. We are interested in exploring statistical algorithms that could lead to generalized procedural map generators.