Learn all about Multi-Agent Path Finding (MAPF)
E. Lam, P. Le Bodic, D. Harabor and P. Stuckey. Branch-and-Cut-and-Price for Multi-Agent Pathfinding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages (in print), 2019.
Abstract: There are currently two broad strategies for optimal Multi-agent Pathfinding (MAPF): (1) search-based methods, which model and solve MAPF directly, and (2) compilation-based solvers, which reduce MAPF to instances of well-known combinatorial problems, and thus, can benefit from advances in solver techniques. In this work, we present an optimal algorithm, BCP, that hybridizes both approaches using branch-and-cut-and-price, a decomposition framework developed for mathematical optimization. We formalize BCP and compare it empirically against CBSH and CBSH-RM, two leading search-based solvers. Conclusive results on standard benchmarks indicate that its performance exceeds the state-of-the-art: solving more instances on smaller grids and scaling reliably to 100 or more agents on larger game maps.
The code is available on bitbucket.