What Matters in Hierarchical Search for Solving Combinatorial Problems?

TMLR Paper4091 Authors

Abstract:

Combinatorial problems, particularly the notorious NP-hard tasks, remain a significant challenge for AI research. A common approach to addressing them combines search with learned heuristics. Recent methods in this domain utilize hierarchical planning, executing strategies based on subgoals. Our goal is to advance research in this area and establish a solid conceptual and empirical foundation.
Specifically, we identify the following key obstacles, whose presence favors the choice of hierarchical search methods: _hard-to-learn value functions_, _complex action spaces_, _presence of dead ends in the environment_, or _training data collected from diverse sources_. Through in-depth empirical analysis, we establish that hierarchical search methods consistently outperform standard search methods across these dimensions, and we formulate insights for future research. On the practical side, we also propose a consistent evaluation guidelines to enable meaningful comparisons between methods and reassess state-of-the-art algorithms.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Marc_Lanctot1

Submission Number: 4091