Intentional Computational Level Design
This work addresses the challenge of intentional level design for video game developers, but it is incremental as it builds on existing procedural generation methods.
The paper tackled the problem of generating video game levels that are not only playable but also focus on specific game mechanics, using constrained evolutionary and quality-diversity algorithms to create scenes in Super Mario Bros. The result showed that three simulation approaches—Limited Agents, Punishing Model, and Mechanics Dimensions—successfully generated scenes that allowed players to encounter or use targeted mechanics with different properties.
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.