I can jump! Exploring search algorithms for simulating platformer players

Abstract

Platformer games let players solve real-time, physics-based puzzles by jumping and moving around to reach different goals. Designing levels for this context is a non-trivial task; the placement of well-timed jumps, moving platforms, interesting traps, etc., has a complex relationship to in-game challenge and the existence of possible solutions. In this work, we describe three different search algorithms (A∗, MCTS and RRT) that could be used to simulate player behaviour in the platformer domain. We evaluate and compare the three approaches applied to three non-trivial levels, showing a possible iterative workflow of use to designers, and research progress in designing search algorithms for platformer games.