2022/06/01 by Michał Zawalski, Zawalski, Michał, Michał Tyrolski +15 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #I.2.6 #I.2.8 #Logic, Reasoning, and Knowledge #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2206.00702
openalex publication_date 2022/06/01 · openalex created_date 2022/06/13 · openalex updated_date 2026/07/28
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.