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.
منابع مشابه
Conflict-Based Search for Optimal Multi-Agent Path Finding
In the multi agent path finding problem (MAPF) paths should be found for several agents, each with a different start and goal position such that agents do not collide. Previous optimal solvers applied global A*-based searches. We present a new search algorithm called Conflict Based Search (CBS). CBS is a two-level algorithm. At the high level, a search is performed on a tree based on conflicts ...
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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