Incremental multi-agent path finding

2021 ◽  
Vol 116 ◽  
pp. 220-233
Author(s):  
Fatih Semiz ◽  
Faruk Polat
Keyword(s):  
Path Finding ◽  
Multi Agent

scholarly journals Multi-Agent Path Finding with Deadlines

2018 ◽  
Author(s):  
Hang Ma ◽  
Glenn Wagner ◽  
Ariel Felner ◽  
Jiaoyang Li ◽  
T. K. Satish Kumar ◽  
...  
Keyword(s):  
Flow Problem ◽  
Search Algorithms ◽  
The Other ◽  
Path Finding ◽  
Combinatorial Search ◽  
Programming Formulation ◽  
Commodity Flow ◽  
Linear Programming Formulation ◽  
Multi Agent ◽  
Integer Linear Programming Formulation

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.

Adversarial Multi-Agent Path Finding is Intractable

2021 ◽  
Author(s):  
Marika Ivanova ◽  
Pavel Surynek
Keyword(s):  
Path Finding ◽  
Multi Agent

A Decentralized Token-Based Negotiation Approach for Multi-Agent Path Finding

2021 ◽  
pp. 264-280
Author(s):  
Cihan Eran ◽  
M. Onur Keskin ◽  
Furkan Cantürk ◽  
Reyhan Aydoğan
Keyword(s):  
Path Finding ◽  
Multi Agent

Train Route Planning as a Multi-agent Path Finding Problem

2021 ◽  
pp. 237-246
Author(s):  
Mauricio Salerno ◽  
Yolanda E-Martín ◽  
Raquel Fuentetaja ◽  
Alba Gragera ◽  
Alberto Pozanco ◽  
...  
Keyword(s):  
Route Planning ◽  
Path Finding ◽  
Multi Agent

scholarly journals Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 974-989
Author(s):  
AYSU BOGATARKAN ◽  
ESRA ERDEM
Keyword(s):  
Answer Set Programming ◽  
Search Problem ◽  
Path Finding ◽  
Combinatorial Search ◽  
Transportation Modes ◽  
Optimal Resource ◽  
Multi Agent ◽  
Nonexistence Of Solutions ◽  
Velocity Constraints ◽  
Answer Set

AbstractThe multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming.

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