  Planning is hard. The use of subgoals can make planning more tractable, but
selecting these subgoals is computationally costly. What algorithms might
enable us to reap the benefits of planning using subgoals while minimizing the
computational overhead of selecting them? We propose visual scoping, a strategy
that interleaves planning and acting by alternately defining a spatial region
as the next subgoal and selecting actions to achieve it. We evaluated our
visual scoping algorithm on a variety of physical assembly problems against two
baselines: planning all subgoals in advance and planning without subgoals. We
found that visual scoping achieves comparable task performance to the subgoal
planner while requiring only a fraction of the total computational cost.
Together, these results contribute to our understanding of how humans might
make efficient use of cognitive resources to solve complex planning problems.
