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Multi-Agent Path Finding with Deadlines

We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally.... Full description

Journal Title: arXiv.org Jun 11, 2018
Main Author: Ma, Hang
Other Authors: Wagner, Glenn , Felner, Ariel , Li, Jiaoyang , Satish Kumar, T , Koenig, Sven
Format: Electronic Article Electronic Article
Language: English
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recordid: proquest2073450166
title: Multi-Agent Path Finding with Deadlines
format: Article
creator:
  • Ma, Hang
  • Wagner, Glenn
  • Felner, Ariel
  • Li, Jiaoyang
  • Satish Kumar, T
  • Koenig, Sven
subjects:
  • Linear Programming
  • Algorithms
  • Solvers
  • Search Algorithms
  • Apexes
  • Multiagent Systems
  • Integer Programming
  • Combinatorial Analysis
  • Flow Mapping
  • Optimization
  • Artificial Intelligence
  • Multiagent Systems
  • Robotics
ispartof: arXiv.org, Jun 11, 2018
description: We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.
language: eng
source: © ProQuest LLC All rights reserved
identifier:
fulltext: fulltext_linktorsrc
url: Link


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subjectLinear Programming ; Algorithms ; Solvers ; Search Algorithms ; Apexes ; Multiagent Systems ; Integer Programming ; Combinatorial Analysis ; Flow Mapping ; Optimization ; Artificial Intelligence ; Multiagent Systems ; Robotics
descriptionWe formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.
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titleMulti-Agent Path Finding with Deadlines
descriptionWe formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.
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abstractWe formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.
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pubCornell University Library, arXiv.org
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date2018-06-11