While formal physics education typically begins in high school or college, humans develop sophisticated intuitions about the physical world long before entering a classroom. Even young children can predict whether a tower of blocks will fall over, how much weight a tree branch can support, or how far a ball will roll if kicked. This intuition about the physical world is a core part of human intelligence, contributing to our everyday commonsense knowledge, but it is still quite difficult to engineer systems that match the robustness and performance of this kind of human intelligence and that would enable robots and other machines to safely and intelligently interact with the real physical world. The current project aims to “reverse engineer” this aspect of human intelligence to determine what internal processes people are using to reason about the physical world. This project relies on a custom physics simulator that can express a wide range of possible physics, many of which differ from those experienced on Earth (e.g., gravity that is a little too strong or an unnatural relationship between force and motion). Human participants and AI machines will judge which physical laws seem most natural or correct and infer unseen parts of a scene using only the motion of visible elements. The comparison of human and machine strategies on identical tasks will uncover the representational commitments of each while advancing state-of-the-art methods for evaluating AI systems and pro