AAAI-23MAPFWorkshop

webmaster: Sven Koenig

AAAI-23 Workshop on Multi-Agent Path Finding

Welcome!

Multi-Agent Path Finding (MAPF) is the problem of computing collision-free paths for a team of agents from their current locations to given destinations in a known environment. Application examples include autonomous aircraft towing vehicles, automated warehouse systems, office robots, and game characters in video games. Solving the MAPF problem optimally is NP-hard for many objectives, such as minimizing the sum of the travel costs or the makespan, and can even be NP-hard to approximate. Yet, practical systems must find high-quality collision-free paths for the agents quickly because shorter paths result in higher throughput or lower operating costs (since fewer agents are required). For information on MAPF, including tutorials, publications, software, benchmark instances, teaching material, and a mailing list, please direct your web browser to mapf.info.

In recent years, many researchers have explored different variants of the MAPF problem as well as different approaches with different properties. Also, different applications have been studied in artificial intelligence, robotics, and theoretical computer science. The purpose of this workshop is to bring these researchers together to present their research, discuss future research directions, and cross-fertilize the different communities. Researchers and practitioners whose research might apply to MAPF or who might be able to use MAPF techniques in their research are welcome.

All submissions that relate to collision-free path planning or navigation for multiple agents are welcome, including but not limited to:

  • Search-, rule-, reduction-, reactive-, and learning-based MAPF planners
  • Combination of MAPF and task allocation, scheduling, and execution monitoring
  • Variants and generalizations of the MAPF problem
  • Real-world applications of MAPF planners
  • Customization of MAPF planners for actual robots (including kinematic and dynamic constraints)
  • Multi-agent reinforcement learning for centralized and decentralized MAPF
  • MAPF for agents with motion uncertainty, communication limitation, and environment change
  • Standardization of MAPF terminology and benchmarks

Submissions can contain relevant work in all possible stages, including work that was recently published, is under submission elsewhere, was only recently finished, or is still ongoing. Authors of papers published or under submission elsewhere are encouraged to submit these papers or short versions (including abstracts) of them to the workshop to educate other researchers about their work, as long as resubmissions are clearly labeled to avoid copyright violations. Position papers and surveys are also welcome. Submissions will go through a light review process to ensure a fit with the topic of the workshop and acceptable quality. Non-archival workshop notes will be produced containing the material presented at the workshop.

Information for Participants

This workshop is part of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), which will be held in Washington, DC. We plan for this workshop to be an in-person workshop and are exploring opportunities for accommodating remote participation.

Submission Link: https://cmt3.research.microsoft.com/WoMAPF2023/Submission/Index

Important Dates:

  • Paper submission deadline: Oct 21, 2022
  • Paper notification: Nov 16, 2022
  • Final version: Dec 14, 2022

Note: all deadlines are “anywhere on earth” (UTC-12)

Schedule

Date: Feb 13 or 14, 2023, TBD.

Organizing Committee

  • Jiaoyang Li, Carnegie Mellon University, (jiaoyanl@andrew.cmu.edu)
  • Zhongqiang Ren, Carnegie Mellon University, (zhongqir@andrew.cmu.edu)
  • Han Zhang, University of Southern California, (zhan645@usc.edu)
  • Zhe Chen, Monash University, (zhe.chen@monash.edu)

Advisory Board

  • Sven Koenig, University of Southern California
  • Howie Choset, Carnegie Mellon University
  • Peter Stuckey, Monash University

Previous MAPF workshops


(last updated in 2022)