  The diversity of agent behaviors is an important topic for the quality of
video games and virtual environments in general. Offering the most compelling
experience for users with different skills is a difficult task, and usually
needs important manual human effort for tuning existing code. This can get even
harder when dealing with adaptive difficulty systems. Our paper's main purpose
is to create a framework that can automatically create behaviors for game
agents of different difficulty classes and enough diversity. In parallel with
this, a second purpose is to create more automated tests for showing defects in
the source code or possible logic exploits with less human effort.
