Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning

TitleRapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Publication TypeJournal Article
Year of Publication2020
AuthorsAllen, KR, Smith, KA, Tenenbaum, JB
JournalProceedings of the National Academy of Sciences
Pagination201912341
Date Published11/2021
ISSN0027-8424
Keywordsintuitive physics, physical problem solving, tool use
Abstract

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.

URLhttp://www.pnas.org/lookup/doi/10.1073/pnas.1912341117
DOI10.1073/pnas.1912341117
Short TitleProc Natl Acad Sci USA
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