mlsanityData to 7 Oct 2026

Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

arXiv 1702.05515 ↗First posted 17 Feb 2017cs.AI cs.MA cs.ROcited by 10

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Abstract (arXiv)

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-world scenarios and four research directions that address them. We emphasize the importance of addressing these issues as opposed to developing faster methods for the standard formulation of the MAPF problem.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

10 papers.

  1. 2021Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidancecited by 8
  2. 2022Graph-Based Multi-Robot Path Finding and Planningcited by 7
  3. 2022Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Taskscited by 4
  4. 2022The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applicationscited by 3
  5. 2021DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologiescited by 1
  6. 2021Decentralised Approach for Multi Agent Path Findingcited by 0
  7. 2023An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Informationcited by 0
  8. 2023Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Agent Path Findingcited by 0
  9. 2023MASP: Scalable GNN-based Planning for Multi-Agent Navigationcited by 0
  10. 2026Unassigned Agents in Compilation-based Multi-agent Path Findingcited by 0