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Branch-and-Cut-and-Price for Multi-Agent Pathfinding

Edward Lam(Edward.Lam***at***monash.edu)
Pierre Le Bodic(Pierre.LeBodic***at***monash.edu)
Daniel Harabor(Daniel.Harabor***at***monash.edu)
Peter J. Stuckey(Peter.Stuckey***at***monash.edu)

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.

Keywords: multi-agent, planning, artificial intelligence, shortest path, column generation

Category 1: Combinatorial Optimization (Branch and Cut Algorithms )

Category 2: Integer Programming (0-1 Programming )

Category 3: Applications -- OR and Management Sciences (Transportation )

Citation: (IJCAI 2019) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Main track. Pages 1289-1296. https://doi.org/10.24963/ijcai.2019/179

Download: [PDF]

Entry Submitted: 08/20/2019
Entry Accepted: 09/03/2019
Entry Last Modified: 08/20/2019

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