发表机构
Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DIVE策略，通过最佳边界搜索与深度优先搜索结合，在保持最优性的同时减少搜索中断、提供早期可行解并降低内存占用。
AI 中文摘要
冲突搜索（CBS）是多智能体路径规划（MAPF）中领先的精确算法，但其高层节点选择规则通常被视为固定的实现细节。标准的最佳优先搜索在最小化扩展节点和闭合最优性证明方面表现强劲，但可能维护较大的前沿集合，中断父子扩展序列，并且在终止前无法提供可行解。本文将节点选择作为精确CBS的一等设计选择进行研究。我们引入了双信息引导的垂直扩展（DIVE），这是一种在搜索深度内以最佳边界为导向、以深度为优先的策略。DIVE从当前最佳边界前沿开始每次搜索深度，跟随有希望的子节点以利用父子局部性，并使用可行解剪枝来限制无效的探索。我们通过分支定界视角形式化CBS节点选择，证明遍历策略可以在不影响精确性的情况下更改，并分析了扩展节点数、搜索深度中断、队列大小和原始-对偶边界进展之间的权衡。该分析预测了三个互补的极端情况。最佳优先搜索节点高效，迭代加深内存高效，而DIVE在保持常规最佳边界重定位的同时搜索深度高效。在标准MAPF基准上的实验支持了这一权衡图。DIVE一致地减少了搜索深度中断，提供了带有认证间隙的早期可行解，使用的队列内存远少于最佳优先搜索，并且在密集或内存受限的情况下受益于热启动和简单的响应变体。
英文摘要
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.
