Final CFP: 6th International Workshop on Multi-Agent Path Finding @ AAAI 2025
The Sixth International Workshop on Multi-Agent Path Finding (MAPF) at AAAI 2025 brings together researchers from artificial intelligence, robotics, and theoretical computer science to discuss and share research and future directions in collision-free path planning or navigation for multiple agents. Submit your work in any format by November 24, 2024.
The Sixth International Workshop on Multi-Agent Path Finding (MAPF) is a one-day workshop at the 39th AAAI Conference on Artificial Intelligence (AAAI) 2025 in Philadelphia, Pennsylvania, USA. The workshop aims to bring together researchers from artificial intelligence, robotics, and theoretical computer science to discuss and share their research, future research directions, and cross-fertilize the different communities. The workshop will include invited talks, paper presentations, Q&A sessions, community discussions, and an Industry Panel Session to foster stronger connections between industry and the MAPF research community.
We invite submissions related to collision-free path planning or navigation for multiple agents, including but not limited to search, rule-based, reduction-based, reactive, and learning-based MAPF planners, MAPF solvers for non-grid and non-point agents, MAPF methods for execution monitoring, replanning, and robustness to delays, combination of MAPF and task allocation, scheduling, real-world applications of MAPF planners, multi-agent machine learning for centralized and decentralized MAPF, customization of MAPF planners for actual robots, and standardization of MAPF terminology and benchmarks.
Submissions can be in any format and there is no page limit. All submissions will receive at least two light reviews, and the review process will be single-blind. The submission site can be found at https://cmt3.research.microsoft.com/WoMAPF2025/Submission/Index.
Tags: Multi-Agent Path Finding, AAAI, Artificial Intelligence, Robotics, Workshop, Collision-free path planning, Navigation, Search, Reduction-based, Reactive, Learning-based, Non-grid, Non-point agents, Execution monitoring, Replanning, Robustness, Task allocation, Scheduling, Real-world applications, Multi-agent machine learning, Customization, Standardization