What Matters in Hierarchical Search for Combinatorial Reasoning Problems?

• URL: http://arxiv.org/abs/2406.03361v2
• Date: Tue, 13 Aug 2024 19:56:45 GMT
• Title: What Matters in Hierarchical Search for Combinatorial Reasoning Problems?
• Authors: Michał Zawalski, Gracjan Góral, Michał Tyrolski, Emilia Wiśnios, Franciszek Budrowski, Łukasz Kuciński, Piotr Miłoś,
• Abstract summary: Recent efforts have sought to enhance planning by incorporating hierarchical high-level search strategies, known as subgoal methods. While promising, their performance against traditional low-level planners is inconsistent, raising questions about their application contexts. We identify the attributes pivotal for leveraging the advantages of high-level search: hard-to-learn value functions, complex action spaces, presence of dead ends in the environment, or using data collected from diverse experts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficiently tackling combinatorial reasoning problems, particularly the notorious NP-hard tasks, remains a significant challenge for AI research. Recent efforts have sought to enhance planning by incorporating hierarchical high-level search strategies, known as subgoal methods. While promising, their performance against traditional low-level planners is inconsistent, raising questions about their application contexts. In this study, we conduct an in-depth exploration of subgoal-planning methods for combinatorial reasoning. We identify the attributes pivotal for leveraging the advantages of high-level search: hard-to-learn value functions, complex action spaces, presence of dead ends in the environment, or using data collected from diverse experts. We propose a consistent evaluation methodology to achieve meaningful comparisons between methods and reevaluate the state-of-the-art algorithms.

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