  Many animals, and an increasing number of artificial agents, display
sophisticated capabilities to perceive and manipulate objects. But human beings
remain distinctive in their capacity for flexible, creative tool use -- using
objects in new ways to act on the world, achieve a goal, or solve a problem. To
study this type of general physical problem solving, we introduce the Virtual
Tools game. In this game, people solve a large range of challenging physical
puzzles in just a handful of attempts. We propose that the flexibility of human
physical problem solving rests on an ability to imagine the effects of
hypothesized actions, while the efficiency of human search arises from rich
action priors which are updated via observations of the world. We instantiate
these components in the "Sample, Simulate, Update" (SSUP) model and show that
it captures human performance across 30 levels of the Virtual Tools game. More
broadly, this model provides a mechanism for explaining how people condense
general physical knowledge into actionable, task-specific plans to achieve
flexible and efficient physical problem-solving.
