Analyzing action games
Yegeta Zeleke, Joseph Osborn, Ricardo G. Sanfelice · 2019
Design support tools benefit from rich information about games' emergent behavior. Inventing successful AI players for particular games can help producing some of this information, but this is both labor intensive and limited in that it can generally only reveal that a solution exists and not say that no solution exists or that certain classes of solution exist. We show a generic method for posing and answering feasible-path, optimal-path, and reachable-space queries in action games, and we devise a measure of game level difficulty. We accomplish all this by encoding action videogame characters as hybrid dynamical systems, using Flappy Bird and Super Mario as case studies.