3 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic
Thomy Phan, Benran Zhang, Shao-Hung Chan +1
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the cur…
Confidence-Based Curriculum Learning for Multi-Agent Path Finding
Thomy Phan, Joseph Driscoll, Justin Romberg +1
A wide range of real-world applications can be formulated as Multi-Agent Path Finding (MAPF) problem, where the goal is to find collision-free paths for multiple agents with indivi…
Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search
Shao-Hung Chan, Zhe Chen, Dian-Lun Lin +5
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while minimizing the sum of travel time. Since s…
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Thomy Phan, Taoan Huang, Bistra Dilkina +1
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Ne…