Extended Abstract: Searching with Consistent Prioritization for Multi-Agent Path Finding


  • Hang Ma University of Southern California
  • Daniel Harabor Monash University
  • Peter Stuckey Monash University
  • Jiaoyang Li University of Southern California
  • Sven Koenig University of Southern California




We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time. This paper was published at AAAI 2019.