Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
This work addresses the problem of modeling human physical problem-solving for cognitive science and AI, but it is incremental as it builds on existing planning and simulation methods.
The paper tackled the problem of understanding how people solve physical block-tower reconfiguration tasks by comparing lab actions to online mental simulations, finding a close correspondence between them. The authors developed a planning model that incorporates symbolic, geometric, and dynamic constraints, achieving high quantitative accuracy in explaining participants' actions and judgments.
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy.