{"title":"The computational complexity of multi-agent pathfinding on directed graphs","authors":"Bernhard Nebel","doi":"10.1016/j.artint.2023.104063","DOIUrl":null,"url":null,"abstract":"<div><p><span>While the non-optimizing variant of multi-agent pathfinding on </span>undirected graphs is known to be a polynomial-time problem since almost forty years, a similar result has not been established for directed graphs. In this paper, it will be shown that this problem is NP-complete. For strongly connected directed graphs, however, the problem is polynomial. And both of these results hold even if one allows for synchronous rotations on fully occupied cycles. Interestingly, the results apply also to the so-called graph motion planning feasibility problem on directed graphs.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0004370223002096","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
While the non-optimizing variant of multi-agent pathfinding on undirected graphs is known to be a polynomial-time problem since almost forty years, a similar result has not been established for directed graphs. In this paper, it will be shown that this problem is NP-complete. For strongly connected directed graphs, however, the problem is polynomial. And both of these results hold even if one allows for synchronous rotations on fully occupied cycles. Interestingly, the results apply also to the so-called graph motion planning feasibility problem on directed graphs.
期刊介绍:
The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.