Charles University, Czech Republic
National Institute of Advanced Industrial Science and Technology (AIST), Japan
Multi-agent Path Finding, A*, Heuristic Function, Multi-commodity Flow, Network Flow, Maximum Flow, Makespan Optimality.
Artificial Intelligence and Decision Support Systems
Distributed and Mobile Software Systems
Enterprise Information Systems
Informatics in Control, Automation and Robotics
Intelligent Control Systems and Optimization
Knowledge Engineering and Ontology Development
Planning and Scheduling
Robot and Multi-Robot Systems
Simulation and Modeling
State Space Search
We address the problem of optimal multi-agent path finding (MAPF) in this paper. The task is to find a set
of actions for each agent in know terrain so that each agent arrives to its desired destination from a given
starting position. Agents are not allowed to collide with each other along their paths. Furthermore, a solution
that minimizes the total time is required. In this paper we study search-based algorithms that systematically
explore state space. These algorithms require a good heuristic function that can improve the computational
effectiveness by changing the order in which the states are expanded. We propose such new heuristic, which
is based on relaxation of MAPF solving via its reduction to multi-commodity flow over time expanded graph.
The multi-commodity flow is relaxed to single commodity flow, which can be solved in polynomial time.
We show that our new heuristic is monotone and therefore can be used in search-based algorithms effectively.
We also give theoret
ical analysis of the new heuristic and compare it experimentally with base-line heuristics
that are often used.