Grigorios Mouratidis, Bernhard Nebel, Sven Koenig

You May Split but You Might Work It Out Later: First Steps Toward Merging Nodes in MAPF (Extended Abstract)

Introduction

You may split but you might work it out later: first steps toward merging nodes in mapf (extended abstract). Improve MAPF algorithm CBS by merging similar high-level nodes, preserving optimality. This novel technique reduces duplicate work and significantly lowers expanded nodes.

Abstract

CBS is a state-of-the-art MAPF algorithm whose performance has been enhanced over the years by the introduction of heuristics that focus the search and reasoning techniques that identify specific types of conflicts that can be resolved faster. To further improve the efficiency of CBS, we present a novel idea based on constraint-reasoning techniques that merges similar high-level nodes close to the root of the constraint tree while preserving CBS' optimality. As a result, some of CBS' duplicate work that occurs when expanding similar high-level nodes is avoided. Our first experimental results using a simple CBS variant (ICBS-h) show a significant reduction in the number of expanded high-level nodes on average.

Review

This extended abstract presents an intriguing and potentially impactful novel approach to enhance the efficiency of Conflict-Based Search (CBS) for Multi-Agent Path Finding (MAPF). The central idea of merging "similar" high-level nodes within the CBS constraint tree, particularly close to the root, directly addresses the problem of redundant computations that plague traditional CBS implementations. By leveraging constraint-reasoning techniques to identify these merge opportunities, the authors aim to avoid duplicate work while critically preserving the optimality guarantee of the underlying CBS algorithm. This concept of node merging in this context appears genuinely innovative and offers a fresh perspective on optimizing a state-of-the-art MAPF solver. A significant strength of this work lies in its conceptual novelty and its direct attack on a known inefficiency in CBS. The proposed mechanism of merging nodes to prune redundant search space is a departure from conventional heuristic improvements or specialized conflict resolution strategies, suggesting a deeper structural optimization. The explicit claim of preserving optimality is vital, ensuring that efficiency gains do not come at the expense of solution quality. Furthermore, the initial experimental results, which report a "significant reduction in the number of expanded high-level nodes on average," provide encouraging preliminary evidence for the practical utility and promise of the proposed merging technique. This indicates a strong potential for improving the scalability of CBS for complex MAPF problems. While the preliminary results are highly promising, it is important to recognize the scope of an "Extended Abstract." The evaluation currently relies on a "simple CBS variant (ICBS-h)," which suggests that the generalizability and performance impact of the merging technique against more advanced and heavily optimized CBS algorithms remain to be thoroughly explored. Future work should ideally include a more comprehensive empirical analysis, comparing the proposed method against a broader suite of state-of-the-art CBS variants and across diverse benchmarks. A detailed discussion on the criteria for defining "similarity" between nodes, as well as the computational overhead introduced by the merging process itself, would also significantly strengthen the contribution. Nevertheless, the core idea presented is sound, innovative, and holds substantial promise, making it a compelling contribution that warrants acceptance as an extended abstract and strongly encourages further development into a full paper.

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