# Intentional Computational Level Design

## TL;DR

This paper explores AI-driven procedural level generation in video games, focusing on creating playable scenes centered around specific game mechanics using evolutionary and quality-diversity algorithms.

## Contribution

It introduces three novel simulation approaches for generating mechanic-focused game scenes with constrained evolutionary algorithms.

## Key findings

- All approaches successfully generate mechanic-centric scenes.
- Different methods offer unique advantages and limitations.
- The work advances targeted content generation in game design.

## Abstract

The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.