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Multi-agent path finding (MAPF) attracts considerable attention in artificialintelligence community as well as in robotics, and other fields such aswarehouse logistics. The task in the standard MAPF is to find paths throughwhich agents can navigate from their starting positions to specified individualgoal positions. The combination of two additional requirements makes theproblem computationally challenging: (i) agents must not collide with eachother and (ii) the paths must be optimal with respect to some objective. Twomajor approaches to optimal MAPF solving include (1) dedicated search-basedmethods, which solve MAPF directly, and (2) compilation-based methods thatreduce a MAPF instance to an instance in a different well establishedformalism, for which an efficient solver exists. The compilation-based MAPFsolving can benefit from advancements accumulated during the development of thetarget solver often decades long. We summarize and compare contemporarycompilation-based solvers for MAPF using formalisms like ASP, MIP, and SAT. Weshow the lessons learned from past developments and current trends in the topicand discuss its wider impact.

Compilation-based Solvers for Multi-Agent Path Finding: a Survey, Discussion, and Future Opportunities