What Matters in Hierarchical Search for Combinatorial Reasoning Problems?
<|reference_start|>What Matters in Hierarchical Search for Combinatorial Reasoning Problems?: 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.<|reference_end|>
@article{zawalski2024what,
    title={What Matters in Hierarchical Search for Combinatorial Reasoning
  Problems?},
    author={Micha{l} Zawalski, Gracjan G'oral, Micha{l} Tyrolski, Emilia
  Wi'snios, Franciszek Budrowski, {L}ukasz Kuci'nski, Piotr Mi{l}o's},
    journal={arXiv preprint arXiv:2406.03361},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03361},
    primaryClass={cs.LG}
}