@inproceedings{244bf6eb1c334519907634ea3901fc3d,
title = "Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance",
abstract = "We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other agents in the group. This extension naturally models many real-world applications, where groups of agents are required to collaborate in order to complete a given task. To this end, we formalize the Co-MAPF problem and introduce Cooperative Conflict-Based Search (Co-CBS), a CBS-based algorithm for solving the problem optimally for a wide set of Co-MAPF problems. Co-CBS uses a cooperation-planning module integrated into CBS such that cooperation planning is decoupled from path planning. Finally, we present empirical results on several MAPF benchmarks demonstrating our algorithm{\textquoteright}s properties.",
author = "Nir Greshler and Ofir Gordon and Oren Salzman and Nahum Shimkin",
note = "Publisher Copyright: Copyright {\textcopyright} 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 14th International Symposium on Combinatorial Search, SoCS 2021 ; Conference date: 26-07-2021 Through 30-07-2021",
year = "2021",
language = "الإنجليزيّة",
series = "14th International Symposium on Combinatorial Search, SoCS 2021",
pages = "173--175",
editor = "Hang Ma and Ivan Serina",
booktitle = "14th International Symposium on Combinatorial Search, SoCS 2021",
}