Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, allowing to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer subgoals and the fine control with the shorter ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik's Cube, and inequality proving benchmark INT.

翻译：复杂推理问题中包含状态，这些状态在确定良好行动计划所需的计算成本上各不相同。利用这一特性，我们提出自适应子目标搜索（AdaSubS），这是一种能够自适应调整规划视野的搜索方法。为此，AdaSubS在不同距离上生成多样化的子目标集。采用验证机制快速滤除不可达的子目标，从而集中精力于可行的更远子目标。通过这种方式，AdaSubS既能从较长子目标规划的高效性中获益，又能利用较小子目标实现精细控制，从而有效应对困难的规划问题。我们证明，在三个复杂推理任务——推箱子、魔方和不等式证明基准INT上，AdaSubS显著超越了分层规划算法。