Dual-Informed Vertical Expansion for Multi-Objective Node Selection in Anytime Conflict-Based Search

2026-06-30

AI Summary

This work addresses a key limitation of traditional Conflict-Based Search (CBS)—its fixed best-first node selection strategy, which often leads to frontier explosion, premature interruption of deep search, and an inability to deliver feasible solutions before termination. The paper introduces Dual-Informed Vertical Expansion (DIVE), the first approach that treats node selection as a core design component of CBS. Operating within a branch-and-bound framework, DIVE performs depth-first dives from the current best boundary node, integrating feasible-solution pruning with periodic boundary re-anchoring to balance exploration efficiency and solution quality. Experimental results demonstrate that DIVE substantially reduces dive interruptions, yields feasible solutions with certified optimality gaps earlier than baseline methods, and maintains significantly lower queue memory consumption—advantages that are especially pronounced in dense or memory-constrained scenarios.

Abstract

Conflict-Based Search (CBS) is a leading exact algorithm for Multi-Agent Path Finding (MAPF), but its high-level node-selection rule is usually treated as a fixed implementation detail. Standard best-first selection is strong for minimizing expanded nodes and closing the optimality certificate, yet it can maintain a large frontier, interrupt parent-child expansion sequences, and provide no feasible incumbent until termination. This paper studies node selection as a first-class design choice for exact CBS. We introduce Dual-Informed Vertical Expansion (DIVE), a policy that is best-bound between dives and depth-oriented within a dive. DIVE starts each dive from the current best-bound frontier, follows promising children to exploit parent-child locality, and uses incumbent pruning to limit unproductive excursions. We formalize CBS node selection through a branch-and-bound view, prove that the traversal policy can be changed without affecting exactness, and analyze the resulting trade-offs among expanded nodes, dive breaks, queue size, and primal-dual bound progress. The analysis predicts three complementary extremes. Best-first search is node efficient, iterative deepening is memory efficient, and DIVE is dive efficient while retaining regular best-bound reanchoring. Experiments on standard MAPF benchmarks support this trade-off map. DIVE consistently reduces dive breaks, provides early incumbents with certified gaps, uses substantially less queue memory than best-first search, and benefits from warm starts and simple responsive variants in dense or memory-limited regimes.

Problem

• Multi-Agent Path Finding
• Conflict-Based Search
• Node Selection
• Anytime Search
• Branch-and-Bound

Innovation

• Dual-Informed Vertical Expansion
• Conflict-Based Search
• Multi-Agent Path Finding
• Node Selection Policy
• Branch-and-Bound