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This content will become publicly available on April 11, 2026

Title: Windowed MAPF with Completeness Guarantees
Traditional multi-agent path finding (MAPF) methods try to compute entire collision free start-goal paths, with several algorithms offering completeness guarantees. However, computing partial paths offers significant advantages including faster planning, adaptability to changes, and enabling decentralized planning. Methods that compute partial paths employ a windowed approach and only try to find collision free paths for a limited timestep horizon. While this improves flexibility, this adaptation introduces incompleteness; all existing windowed approaches can become stuck in deadlock or livelock. Our main contribution is to introduce our framework, WinC-MAPF, for Windowed MAPF that enables completeness. Our framework leverages heuristic update insights from single-agent real-time heuristic search algorithms and agent independence ideas from MAPF algorithms. We also develop Single-Step Conflict Based Search (SS-CBS), an instantiation of this framework using a novel modification to CBS. We show how SS-CBS, which only plans a single step and updates heuristics, can effectively solve tough scenarios where existing windowed approaches fail.  more » « less
Award ID(s):
2328671
PAR ID:
10634335
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
AAAI
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
39
Issue:
22
ISSN:
2159-5399
Page Range / eLocation ID:
23323 to 23332
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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