Combining Strengths of Optimal Multi-Agent Path Finding Algorithms

Švancara, Jiří; Barták, Roman

doi:10.5220/0007470002260231

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…1a). These results comply with the observation made for the classical MAPF problem that the CBS-based algorithm performs well on sparse instances, and reduction-based algorithms perform well on smaller, more dense instances ( Švancara and Barták 2019). Surprisingly, SAT-grouped is not always better than SATbasic, especially on the largest maps.…”

Section: Empirical Evaluationsupporting

confidence: 88%

From Classical to Colored Multi-Agent Path Finding

Barták

Ivanová

Švancara

2021

SOCS

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Multi-Agent Path Finding (MAPF) deals with the problem of finding collision-free paths for a set of agents moving in a shared environment, while each agent has specified its own destination. Colored MAPF generalizes MAPF by defining groups of agents that share a set of destination locations. In the paper, we evaluate several approaches to optimally solve colored MAPF problem, namely, a method based on network flows, an extended version of conflict-based search, and two models using Boolean satisfiability. We also investigate methods for obtaining lower bounds on optimal solutions based on constraint and continuous relaxation techniques.

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Section: Empirical Evaluationsupporting

confidence: 88%

From Classical to Colored Multi-Agent Path Finding

Barták

Ivanová

Švancara

2021

SOCS

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“…Our G2V model performs well despite only having access to nodes on shortest paths, potentially a very small fraction of the total number of nodes in the instance. In the shortest paths, there is no explicit information on map type, obstacle density, or map size, heuristics which have previously been used to select algorithms [5,20]. Despite this lack of map information, our G2V model performs quite well, better than any single algorithm in our portfolio and close in performance to our CNN-based models, which have access to much more information.…”

Section: G2vmentioning

confidence: 95%

“…It has been shown that if a MAPF instance can be decomposed into multiple disjoint sub-problems, solving each sub-problem independently and combining their results can be significantly faster than solving the original problem with a MAPF algorithm [20]. Each sub-problem may have different map and agent characteristics, which will affect the runtime of whatever MAPF algorithm is chosen for each sub-problem.…”

mentioning

confidence: 99%

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

Ren,

Sathiyanarayanan,

Ewing

et al. 2021

Preprint

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Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization. While many algorithms have been developed to solve MAPF problems, there is no dominating optimal MAPF algorithm that works well in all types of problems and no standard guidelines for when to use which algorithm. In this work, we develop the deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor), which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms. We improve the performance of our model by including single-agent shortest paths in the instance embedding given to our model and by utilizing supplemental loss functions in addition to a classification loss. We evaluate our model on a large and diverse dataset of MAPF instances, showing that it outperforms all individual algorithms in its portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We also provide an analysis of algorithm behavior in our dataset to gain a deeper understanding of optimal MAPF algorithms' strengths and weaknesses to help other researchers leverage different heuristics in algorithm designs.

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Hybrid Path Planning for UAV Traffic Management

Zehavi

Agmon

2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Combining Strengths of Optimal Multi-Agent Path Finding Algorithms

Cited by 4 publications

References 7 publications

From Classical to Colored Multi-Agent Path Finding

From Classical to Colored Multi-Agent Path Finding

MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

Hybrid Path Planning for UAV Traffic Management

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