Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
This work is incremental, as it reviews and synthesizes existing challenges without introducing new methods or data.
The paper addresses the challenge of adapting Multi-Agent Path Finding (MAPF) methods to real-world scenarios, highlighting key issues and proposing four research directions to overcome them, rather than focusing on speed improvements for standard formulations.
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.