Journal article

Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding

Shao-Hung Chan, Jiaoyang Li, Graeme Gange, Daniel Harabor, Peter J Stuckey, Sven Koenig

THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE | Published : 2022

Open access

Abstract

Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents that minimize the sum of path costs. EECBS is a leading two-level algorithm that solves MAPF bounded-suboptimally, that is, within some factor w of the minimum sum of path costs C*. It uses focal search to find bounded-suboptimal paths on the low level and Explicit Estimation Search (EES) to resolve collisions on the high level. EES keeps track of a lower bound LB on C* to find paths whose sum of path costs is at most w LB in order to solve MAPF bounded-suboptimally. However, the costs of many paths are often much smaller than w times their minimum path costs, meaning that the sum of path cost..

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University of Melbourne Researchers

Grants

Awarded by National Science Foundation (NSF)


Awarded by Australian Research Council


Awarded by Direct For Computer & Info Scie & Enginr; Div Of Information & Intelligent Systems


Awarded by Division Of Computer and Network Systems; Direct For Computer & Info Scie & Enginr


Awarded by Division Of Undergraduate Education; Direct For Education and Human Resources


Funding Acknowledgements

The research at the University of Southern California was supported by the National Science Foundation (NSF) under grants 1409987, 1724392, 1817189, 1837779, 1935712, and 2112533 as well as a gift from Amazon. The research at Monash University was supported by the Australian Research Council under Discovery Grants DP190100013 and DP200100025 as well as a gift from Amazon.