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Anytime Multi-Agent Path Finding via Machine Learning-Guided Large Neighborhood Search

Taoan Huang, Jiaoyang Li, Sven Koenig, Bistra Dilkina
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we improve the current state-of-the-art anytime solver MAPF-LNS, that first finds an initial solution fast and then repeatedly replans the paths of subsets of agents via Large Neighborhood  ...  Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents in a common environment.  ...  MAPF-LNS (Li et al. 2021b ) is a state-of-the-art anytime MAPF solver that uses Large Neighborhood Search (LNS) (Ahuja et al. 2002) .  ... 
doi:10.1609/aaai.v36i9.21168 fatcat:th562mpwe5gm7aign3o4zrzl4m

Anytime Multi-Agent Path Finding via Large Neighborhood Search

Jiaoyang Li, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Sven Koenig
2021 Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence   unpublished
Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for multiple agents. Algorithms for solving MAPF can be categorized on a spectrum.  ...  large problems, and that subsequently improve the solution quality to near-optimal as time progresses by replanning subgroups of agents using Large Neighborhood Search.  ...  Large Neighborhood Search for MAPF Large Neighborhood Search (LNS) [Shaw, 1998 ] is a popular meta-heuristic for finding good solutions to challenging discrete optimization problems.  ... 
doi:10.24963/ijcai.2021/568 fatcat:jwqs2xpztrglfl4p4c5fgewxiy

MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search

Jiaoyang Li, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Sven Koenig
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Multi-Agent Path Finding (MAPF) is the problem of planning collision-free paths for multiple agents in a shared environment.  ...  In this paper, we propose a novel algorithm MAPF-LNS2 based on large neighborhood search for solving MAPF efficiently.  ...  Instead of giving up on the previous search effort and restarting from scratch, we make use of the infeasible set of paths produced by a MAPF algorithm and try to repair it via Large Neighborhood Search  ... 
doi:10.1609/aaai.v36i9.21266 fatcat:n3hzvrm5bfbcjjkmmxlwq3oljy

Planning and Learning: A Review of Methods involving Path-Planning for Autonomous Vehicles [article]

Kevin Osanlou, Christophe Guettier, Tristan Cazenave, Eric Jacopin
2022 arXiv   pre-print
Next, we study some successful approaches combining neural networks for path-planning. Lastly, we focus on temporal planning problems with uncertainty.  ...  Moreover, ANYA also guarantees to find optimal any-angle paths. C. Anytime Planning In some situations a path needs to be computed quickly.  ...  HPA* proceeds to divide the environment into square clusters with connections, making an abstract search graph which is searched to find a shortest path.  ... 
arXiv:2207.13181v1 fatcat:o7o4ss2l2vbfpcb4kal34u3mj4

Speeding Up Convergence [chapter]

2013 Asymptotic Analysis and Perturbation Theory  
neighborhood as large as possible.  ...  The task is to find the shortest path from start to goal through states in the neighborhood.  ...  Lemma 5 Under assumptions A1-4, assume that FALCONS follows a path Proof: The proof is the same as that for Lemma 5, except that it uses Corollary 4(5) instead of Lemma 4(5).  ... 
doi:10.1201/b15165-4 fatcat:6zid6eprdnd43oecngqnhfdkp4

Dynamically Pruned A* for re-planning in navigation meshes

Wouter van Toll, Roland Geraerts
2015 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
However, DPA* prunes the search using only the previous path and its relation to the dynamic event.  ...  The A* algorithm computes optimal paths through the dual graph of this mesh. When an obstacle is inserted or deleted, the mesh changes and agents should re-plan their paths.  ...  These are related to anytime algorithms that iteratively improve a sub-optimal path [27] - [29] . However, remembering the A* search space of each agent is not feasible for large crowds.  ... 
doi:10.1109/iros.2015.7353649 dblp:conf/iros/TollG15 fatcat:6x7fcpf2p5fmdpkgakwhpcfvuq

GRASP and path-relinking for Coalition Structure Generation [article]

Nicola Di Mauro, Teresa M.A. Basile, Stefano Ferilli, Floriana Esposito
2011 arXiv   pre-print
In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures.  ...  The problem of finding the optimal coalition structure is NP-complete.  ...  However, DP is not an anytime algorithm, and has a large memory requirement. Indeed, for each coalition C it computes the tables t 1 (C) and t 2 (C).  ... 
arXiv:1103.1157v2 fatcat:dtekkqj32rgp3ayj3sdmc5ihwm

Spatially-Distributed Missions with Heterogeneous Multi-Robot Teams

Eduardo Feo-Flushing, Luca Maria Gambardella, Gianni A. Di Caro
2021 IEEE Access  
Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms.  ...  This work is about mission planning in teams of mobile autonomous agents.  ...  The findings herein reflect the work, and are solely the responsibility of the author[s].  ... 
doi:10.1109/access.2021.3076919 fatcat:f5kfgsr3uzamhe7ptdojng4ldq

Pretrained Cost Model for Distributed Constraint Optimization Problems

Yanchen Deng, Shufeng Kong, Bo An
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Previously, Machine Learning (ML) has been largely applied to solve combinatorial optimization problems by learning effective heuristics.  ...  or backtracking search.  ...  , belief propagation, region optimal method, and large neighborhood search.  ... 
doi:10.1609/aaai.v36i9.21164 fatcat:wh3y2llhk5fqvmzrvzqwdzqtca

Pretrained Cost Model for Distributed Constraint Optimization Problems [article]

Yanchen Deng, Shufeng Kong, Bo An
2021 arXiv   pre-print
Previously, Machine Learning (ML) has been largely applied to solve combinatorial optimization problems by learning effective heuristics.  ...  or backtracking search.  ...  Baselines We consider four types of baselines: local search, belief propagation, region optimal method, and large neighborhood search.  ... 
arXiv:2112.04187v2 fatcat:tn3cmsrrmzeczo3a22b7momspu

Explorative anytime local search for distributed constraint optimization

Roie Zivan, Steven Okamoto, Hilla Peled
2014 Artificial Intelligence  
A general framework that enhances distributed local search algorithms for DCOPs with the anytime property is proposed.  ...  It reveals the advantage of the use of the proposed heuristics in the anytime framework over state-of-the-art local search algorithms. 1  ...  state the most in its neighborhood replaces its assignment.  ... 
doi:10.1016/j.artint.2014.03.002 fatcat:rccwfkidmzendnsddnbu77zrfe

Toward Understanding the Impact of User Participation in Autonomous Ridesharing Systems [article]

Wen Shen, Rohan Achar, Cristina V. Lopes
2018 arXiv   pre-print
We also find it very rewarding to explore new methods for scalable multi-agent simulations. ACKNOWLEDGMENTS This work was supported by National Science Foundation through grant no. CCF-1526593.  ...  However, multi-agent simulations typically require interactions between different applications (agents) (Macal and North 2010) .  ... 
arXiv:1803.06464v2 fatcat:j47vjsqh7re7pl7kfav2dgq2mu

A Tutorial on Optimization for Multi-Agent Systems

J. Cerquides, A. Farinelli, P. Meseguer, S. D. Ramchurn
2013 Computer journal  
R., and [4] Shehory, O. and Kraus, S. (1998) Methods for task Giovannucci, A. (2009) An anytime algorithm for allocation via agent coalition formation.  ...  agents in a multi-agent system.  ... 
doi:10.1093/comjnl/bxt146 fatcat:tvebsldktvb2poo7zbly7doone

Network-guided multi-robot path planning in discrete representations

R Luna, K E Bekris
2010 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems  
The objective is to compute paths that are collisionfree, minimize the occurrence of deadlocks, as well as the time it takes to reach the robots' goals.  ...  The nodes compute paths for the robots within their communication range given information about robots only in their vicinity and communicating only with neighbors.  ...  The sensors first compute alternative local paths for robots in their vicinity and then coordinate to find an assignment of paths to robots.  ... 
doi:10.1109/iros.2010.5649064 dblp:conf/iros/LunaB10 fatcat:jkjsmhhgezfeples6n7i4uwqsu

Distributed Constraint Optimization Problems and Applications: A Survey

Ferdinando Fioretto, Enrico Pontelli, William Yeoh
2018 The Journal of Artificial Intelligence Research  
The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications.  ...  This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each  ...  It is also not an anytime algorithm as it is a best-first search algorithm.  ... 
doi:10.1613/jair.5565 fatcat:zeou5gji5fas3h27ab4jxvtwkq
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