  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.
