Authors:
Jiri Svancara
1
and
Pavel Surynek
2
Affiliations:
1
Charles University, Czech Republic
;
2
National Institute of Advanced Industrial Science and Technology (AIST), Japan
Keyword(s):
Multi-agent Path Finding, A*, Heuristic Function, Multi-commodity Flow, Network Flow, Maximum Flow, Makespan Optimality.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Mobile Agents
;
Multi-Agent Systems
;
Planning and Scheduling
;
Robot and Multi-Robot Systems
;
Simulation and Modeling
;
Software Engineering
;
State Space Search
;
Symbolic Systems
Abstract:
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 theore
tical analysis of the new heuristic and compare it experimentally with base-line heuristics
that are often used.
(More)