Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
This addresses the challenge of understanding and replicating human-like tool use and physical reasoning in artificial agents, though it is incremental in modeling specific cognitive mechanisms.
The paper tackled the problem of flexible physical problem solving in humans by introducing the Virtual Tools game and proposing the Sample, Simulate, Update (SSUP) model, which captures human performance across 30 levels of the game.
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.