Subgoal Search For Complex Reasoning Tasks

Abstract:Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propose Subgoal Search (kSubS) method. Its key component is a learned subgoal generator that produces a diversity of subgoals that are both achievable and closer to the solution. Using subgoals reduces the search space and induces a high-level search graph suitable for efficient planning. In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework. We show that a simple approach of generating $k$-th step ahead subgoals is surprisingly efficient on three challenging domains: two popular puzzle games, Sokoban and the Rubik's Cube, and an inequality proving benchmark INT. kSubS achieves strong results including state-of-the-art on INT within a modest computational budget.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2108.11204 [cs.AI]

(or arXiv:2108.11204v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2108.11204 Focus to learn more arXiv-issued DOI via DataCite

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