Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2021

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v35i13.17341