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arXiv · 2406.03361

What Matters in Hierarchical Search for Combinatorial Reasoning Problems?

Michał Zawalski·Gracjan Góral·Michał Tyrolski·Emilia Wiśnios·Franciszek Budrowski·Marek Cygan·Łukasz Kuciński·Piotr Miłoś
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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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Michał Zawalski, Gracjan Góral, Michał Tyrolski, Emilia Wiśnios, Franciszek Budrowski, Marek Cygan, Łukasz Kuciński, Piotr Miłoś. 2024-06-05. What Matters in Hierarchical Search for Combinatorial Reasoning Problems?. https://arxiv.org/abs/2406.03361

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