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.

翻译：规划是艰难的。 次级目标的使用可以使规划更便于推进,但选择这些次级目标在计算上成本很高。 什么样的算法可以让我们从利用次级目标进行规划中获益,同时最大限度地减少选择这些次级目标的计算间接费用? 我们提出了目视范围界定战略,这一战略将规划与行动交替确定空间区域为下一个次级目标,并选择实现该目标的行动。 我们根据两个基线评估了我们关于各种物理组装问题的视觉范围界定算法:预先规划所有次级目标,在没有次级目标的情况下进行规划。 我们发现,视觉范围界定可以实现与次级目标规划者相似的任务业绩,而只需要计算总成本的一小部分。 这些结果共同有助于我们了解人类如何有效地利用认知资源解决复杂的规划问题。