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具有不同计算成本的状态，这些成本用于确定良好的行动方案。利用这一特性，我们提出了自适应子目标搜索（AdaSubS），一种能够自适应调整规划视野的搜索方法。为此，AdaSubS生成在不同距离下多样化的子目标集合。采用验证机制快速过滤掉不可达的子目标，从而专注于可行的远期子目标。通过这种方式，AdaSubS既受益于更长子目标规划的高效性，又保留了较短子目标规划的精细控制能力，因此能很好地扩展到困难的规划问题。我们展示了AdaSubS在三项复杂推理任务：推箱子游戏、魔方和不等式证明基准INT上，显著超越了分层规划算法。