Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
arxiv(2022)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要