What Matters in Hierarchical Search for Combinatorial Reasoning Problems?

Gracjan Góral, Michał Zawalski, Michał Tyrolski, Emilia Wiśnios, Franciszek Budrowski, Marek Cygan, Łukasz Kuciński, Piotr Miłoś

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.