Automated Testing in Super Metroid with Abstraction-Guided Exploration
Ross Mawhorter, Adam M. Smith · 2023
Machine playtesting systems often aim to demonstrate how to reach certain moments of play. To provide design feedback in a timely manner, they often internally rely on heuristic-guided search. However, there are many types of videogames for which sufficiently accurate heuristics are not available. We use an imperfect abstraction of an underlying game to define progress scores, and show that combining these scores yields a highly effective cell selection heuristic for use in the Go-Explore algorithm. We demonstrate the impact of this approach in automated gameplay for Super Metroid (involving mandatory item collection, destructible blocks, and backtracking) using a tile-based abstraction of the game that only models a small subset of the game’s mechanics. Surprisingly, our abstraction guidance mechanism is able to explore this complex game several orders of magnitude more efficiently than past work with similar exploration methods in Montezuma’s Revenge.