论文标题
快速而精确：通过自适应子搜索调整计划范围
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
论文作者
论文摘要
复杂的推理问题包含确定良好行动计划所需的计算成本各不相同的状态。利用此属性，我们提出了自适应子观念搜索（ADASUBS），这是一种适应性地调整计划范围的搜索方法。为此，ADASUBS在不同距离上产生各种子目标。采用验证机制迅速过滤掉无法达到的子观念，从而使人专注于可行的进一步子目标。这样，ADASUBS从较长的子目标的计划效率和对较短的计划的良好控制中受益，因此可以很好地扩展到困难的计划问题。我们表明，ADASUB在三个复杂的推理任务上显着超过了层次规划算法：Sokoban，The Rubik的立方体和不平等现象证明了基准INT。
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.
