Conflict-Based Search (CBS) is a leading two-level algorithm for optimal Multi-Agent Path Finding (MAPF). The main step of CBS is to expand nodes by resolving conflicts (where two agents collide). Choosing the 'right' conflict to resolve can greatly speed up the search. CBS first resolves conflicts where the costs (g-values) of the resulting child nodes are larger than the cost of the node to be split. However, the recent addition of high-level heuristics to CBS and expanding nodes according to f = g + h reduces the relevance of this conflict prioritization method. Therefore, we introduce an expanded categorization of conflicts, which first resolves conflicts where the f-values of the child nodes are larger than the f-value of the node to be split, and present a method for identifying such conflicts. We also enhance all known heuristics for CBS by using information about the cost of resolving certain conflicts with only a small computational overhead. Finally, we experimentally demonstrate that both the expanded categorization of conflicts and the improved heuristics contribute to making CBS even more efficient.
|Title of host publication||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||8|
|State||Published - 2021|
|Event||35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online|
Duration: 2 Feb 2021 → 9 Feb 2021
|Name||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Conference||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Period||2/02/21 → 9/02/21|
Bibliographical noteFunding Information:
The research at Ben-Gurion University of the Negev was supported by the Israel Ministry of Science, ISF grant 844/17 and BSF grant 2017692. The research at Monash University is partially supported by ARC grant DP200100025. The research at the University of Southern California was supported by the National Science Foundation (NSF) under grant numbers 1409987, 1724392, 1817189, 1837779, and 1935712 as well as a gift from Amazon. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, or the U.S. government.
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