Subgoal Search For Complex Reasoning Tasks

Bibliographic Details
Title: Subgoal Search For Complex Reasoning Tasks
Authors: Czechowski, Konrad, Odrzygóźdź, Tomasz, Zbysiński, Marek, Zawalski, Michał, Olejnik, Krzysztof, Wu, Yuhuai, Kuciński, Łukasz, Miłoś, Piotr
Publication Year: 2021
Collection: Computer Science
Subject Terms: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
More Details: 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.
Comment: NeurIPS 2021
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2108.11204
Accession Number: edsarx.2108.11204
Database: arXiv
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  Data: <searchLink fieldCode="AR" term="%22Czechowski%2C+Konrad%22">Czechowski, Konrad</searchLink><br /><searchLink fieldCode="AR" term="%22Odrzygóźdź%2C+Tomasz%22">Odrzygóźdź, Tomasz</searchLink><br /><searchLink fieldCode="AR" term="%22Zbysiński%2C+Marek%22">Zbysiński, Marek</searchLink><br /><searchLink fieldCode="AR" term="%22Zawalski%2C+Michał%22">Zawalski, Michał</searchLink><br /><searchLink fieldCode="AR" term="%22Olejnik%2C+Krzysztof%22">Olejnik, Krzysztof</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yuhuai%22">Wu, Yuhuai</searchLink><br /><searchLink fieldCode="AR" term="%22Kuciński%2C+Łukasz%22">Kuciński, Łukasz</searchLink><br /><searchLink fieldCode="AR" term="%22Miłoś%2C+Piotr%22">Miłoś, Piotr</searchLink>
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  Data: 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.<br />Comment: NeurIPS 2021
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      – SubjectFull: Computer Science - Artificial Intelligence
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      – SubjectFull: Computer Science - Machine Learning
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      – TitleFull: Subgoal Search For Complex Reasoning Tasks
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