Tomas Rybecky presents Robotics RG
On 2022-01-07 09:00:00 at JP:B-335
Dear colleagues,
let me invite you to the next robotics reading group, which takes place Friday
(7.1) at 9:00 in CIIRC: JP:B-335. Tomas
Rybecky will present the following paper:
Jiaoyang Li, Zhe Chen, Daniel Harabor, Peter J. Stuckey, and Sven Koenig. 2021.
Anytime Multi-Agent Path Finding via Large Neighborhood Search. International
Joint Conference on Artificial Intelligence (IJCAI), pages 4127-4135
Paper link: https://www.ijcai.org/proceedings/2021/0568.pdf
Video call link: https://meet.google.com/jyu-vfim-kbc
RG homepage: https://cw.fel.cvut.cz/wiki/courses/xp33rg2/start
Abstract:
Multi-Agent Path Finding (MAPF) is the challenging problem of computing
collision-free paths for multiple agents. Algorithms for solving MAPF can be
categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that
can find high-quality solutions for small problems. At the other end are
unbounded-suboptimal algorithms that can solve large problems but usually find
low-quality solutions. In this paper, we consider a third approach that
combines
the best of both worlds: anytime algorithms that quickly find an initial
solution using efficient MAPF algorithms from the literature, even for large
problems, and that subsequently improve the solution quality to near-optimal as
time progresses by replanning subgroups of agents using Large Neighborhood
Search. We compare our algorithm MAPFLNS against a range of existing work and
report significant gains in scalability, runtime to the initial solution, and
speed of improving the solution.
let me invite you to the next robotics reading group, which takes place Friday
(7.1) at 9:00 in CIIRC: JP:B-335. Tomas
Rybecky will present the following paper:
Jiaoyang Li, Zhe Chen, Daniel Harabor, Peter J. Stuckey, and Sven Koenig. 2021.
Anytime Multi-Agent Path Finding via Large Neighborhood Search. International
Joint Conference on Artificial Intelligence (IJCAI), pages 4127-4135
Paper link: https://www.ijcai.org/proceedings/2021/0568.pdf
Video call link: https://meet.google.com/jyu-vfim-kbc
RG homepage: https://cw.fel.cvut.cz/wiki/courses/xp33rg2/start
Abstract:
Multi-Agent Path Finding (MAPF) is the challenging problem of computing
collision-free paths for multiple agents. Algorithms for solving MAPF can be
categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that
can find high-quality solutions for small problems. At the other end are
unbounded-suboptimal algorithms that can solve large problems but usually find
low-quality solutions. In this paper, we consider a third approach that
combines
the best of both worlds: anytime algorithms that quickly find an initial
solution using efficient MAPF algorithms from the literature, even for large
problems, and that subsequently improve the solution quality to near-optimal as
time progresses by replanning subgroups of agents using Large Neighborhood
Search. We compare our algorithm MAPFLNS against a range of existing work and
report significant gains in scalability, runtime to the initial solution, and
speed of improving the solution.
External www: https://cw.fel.cvut.cz/wiki/courses/xp33rg2/start