  Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent
path finding. At the high level, CBS repeatedly detects conflicts and resolves
one of them by splitting the current problem into two subproblems. Previous
work chooses the conflict to resolve by categorizing the conflict into three
classes and always picking a conflict from the highest-priority class. In this
work, we propose an oracle for conflict selection that results in smaller
search tree sizes than the one used in previous work. However, the computation
of the oracle is slow. Thus, we propose a machine-learning framework for
conflict selection that observes the decisions made by the oracle and learns a
conflict-selection strategy represented by a linear ranking function that
imitates the oracle's decisions accurately and quickly. Experiments on
benchmark maps indicate that our method significantly improves the success
rates, the search tree sizes and runtimes over the current state-of-the-art CBS
solver.
