# Automatic Generation of Level Maps with the Do What's Possible Representation

## TL;DR

This paper presents a novel method for automatic, scalable level map generation using the 'do what's possible' representation, with parameter tuning and variations to enhance performance and adaptability.

## Contribution

It introduces a new technique employing the 'do what's possible' representation for open-ended map generation, including algorithmic improvements and adaptability testing.

## Key findings

- The method enables indefinite map generation.
- Parameter tuning improves map quality.
- Variations demonstrate versatility and performance enhancements.

## Abstract

Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the {\em do what's possible} representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high-quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.