Improved Anonymous Multi-Agent Path Finding Algorithm

• URL: http://arxiv.org/abs/2312.10572v4
• Date: Sat, 27 Jan 2024 22:57:51 GMT
• Title: Improved Anonymous Multi-Agent Path Finding Algorithm
• Authors: Zain Alabedeen Ali and Konstantin Yakovlev
• Abstract summary: We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the set of agents is confined to a graph. We suggest a specific search algorithm that leverages the idea of exploring the search space not through considering separate search states but rather bulks of them simultaneously. The resultant AMAPF solver demonstrates superior performance compared to the state-of-the-art competitor and is able to solve all publicly available MAPF instances in less than 30 seconds.
• Score: 3.694001372535405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the set of agents is confined to a graph, a set of goal vertices is given and each of these vertices has to be reached by some agent. The problem is to find an assignment of the goals to the agents as well as the collision-free paths, and we are interested in finding the solution with the optimal makespan. A well-established approach to solve this problem is to reduce it to a special type of a graph search problem, i.e. to the problem of finding a maximum flow on an auxiliary graph induced by the input one. The size of the former graph may be very large and the search on it may become a bottleneck. To this end, we suggest a specific search algorithm that leverages the idea of exploring the search space not through considering separate search states but rather bulks of them simultaneously. That is, we implicitly compress, store and expand bulks of the search states as single states, which results in high reduction in runtime and memory. Empirically, the resultant AMAPF solver demonstrates superior performance compared to the state-of-the-art competitor and is able to solve all publicly available MAPF instances from the well-known MovingAI benchmark in less than 30 seconds.

Related papers

• Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding [9.831879504969224]
  Multi-agent path finding (MAPF) is the problem of finding paths for multiple agents such that they do not collide. Finding optimal solutions to MAPF is NP-Hard, yet modern optimal solvers can scale to hundreds of agents and even thousands in some cases. We show how this encoding can be effectively joined with existing encodings, resulting in a novel AS method we call MAPF Algorithm selection via Graph embedding.
  arXiv  Detail & Related papers  (2024-06-16T07:41:58Z)
• Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding [49.730902939565986]
  Multi-Agent Pathfinding problem involves finding a set of conflict-free paths for a group of agents confined to a graph. In this study, we focus on the decentralized MAPF setting, where the agents may observe the other agents only locally. We propose a decentralized multi-agent Monte Carlo Tree Search (MCTS) method for MAPF tasks.
  arXiv  Detail & Related papers  (2023-12-26T06:57:22Z)
• Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results [60.4817465598352]
  We introduce an original variant of Monte-Carlo Tree Search (MCTS) tailored to multi-agent pathfinding. Specifically, we use individual paths to assist the agents with the the goal-reaching behavior. We also use a dedicated decomposition technique to reduce the branching factor of the tree search procedure.
  arXiv  Detail & Related papers  (2023-07-25T12:33:53Z)
• Fine-Grained Complexity Analysis of Multi-Agent Path Finding on 2D Grids [0.190365714903665]
  We show that 2-colored MAPF, where the agents are divided into two teams, each with its own set of targets, remains NP-hard. For the flowtime objective, we establish a tractability frontier based on the number of directions agents can move in. This result sheds new light on the structure of optimal solutions, which may help guide algorithm design for the general problem.
  arXiv  Detail & Related papers  (2023-05-25T17:56:24Z)
• A Theoretical Analysis Of Nearest Neighbor Search On Approximate Near Neighbor Graph [51.880164098926166]
  Graph-based algorithms have demonstrated state-of-the-art performance in the nearest neighbor search (NN-Search) problem. There exists a practice-to-theory gap in the graph-based NN-Search algorithms. We present theoretical guarantees of solving NN-Search via greedy search on ANN-Graph for low dimensional and dense vectors.
  arXiv  Detail & Related papers  (2023-03-10T21:18:34Z)
• Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search [96.31315520244605]
  Arch-Graph is a transferable NAS method that predicts task-specific optimal architectures. We show Arch-Graph's transferability and high sample efficiency across numerous tasks. It is able to find top 0.16% and 0.29% architectures on average on two search spaces under the budget of only 50 models.
  arXiv  Detail & Related papers  (2022-04-12T16:46:06Z)
• A Metaheuristic Algorithm for Large Maximum Weight Independent Set Problems [58.348679046591265]
  Given a node-weighted graph, find a set of independent (mutually nonadjacent) nodes whose node-weight sum is maximum. Some of the graphs airsing in this application are large, having hundreds of thousands of nodes and hundreds of millions of edges. We develop a new local search algorithm, which is a metaheuristic in the greedy randomized adaptive search framework.
  arXiv  Detail & Related papers  (2022-03-28T21:34:16Z)
• Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering [15.99072005190786]
  We introduce multi-goal multi agent path finding (MAPF$MG$) which generalizes the standard discrete multi-agent path finding (MAPF) problem. We suggest two novel algorithms using different paradigms to address MAPF$MG$: a search-based search algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the SMT paradigm, called SMT-Hamiltonian-CBS (SMT-HCBS)
  arXiv  Detail & Related papers  (2020-09-10T22:27:18Z)
• MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models [96.1052289276254]
  This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to the low-performing solver, we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin.
  arXiv  Detail & Related papers  (2020-04-16T16:20:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.