arXiv:2611.07528v1 Announce Type: new Abstract: LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate successors and lazily adding single-agent constraints to explore alternatives. Although LaCAM is complete, it can struggle on dense, corridor-heavy instances because extracting blocking agents from deadlocks requires coordinated moves of several agents, often away from their goals, which PIBT's greedy one-step successors rarely propose and which LaCAM can only reach by enumerating constraints one agent at a time. This paper proposes Goal-Branching, where blocking agents are given temporary goals, each tried as an additional branch of LaCAM's search.
Artificial Intelligence · 2026-11-06
Goal-Branching LaCAM: Temporary Goals as Search Branches in Dense Multi-Agent Pathfinding
arXiv:2611.07528v1 Announce Type: new Abstract: LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate successors and lazily adding single-agent constraints to explore alternatives.
Source and attribution
This is an attributed editorial digest. For the complete study and original reporting, follow the source link.
Reader feedback
0 likes · 0 dislikes