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，不同状态在确定最优行动方案所需的计算代价上存在差异。利用这一特性，我们提出自适应子目标搜索（AdaSubS），一种能够动态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合，并采用验证机制快速筛除不可达的子目标，从而聚焦于可行的深层子目标。通过这种方式，AdaSubS既受益于长程子目标规划的效率性，又保留了短程子目标的精细控制能力，因此能够有效扩展至困难规划问题。实验表明，AdaSubS在三个复杂推理任务（推箱子游戏、魔方还原、不等式证明基准INT）上显著超越分层规划算法。