Inapproximability of Optimal Multi-Agent Pathfinding Problems

Multi-agent pathfinding MAPF is a problem where multiple autonomous agents must find paths to their respective destinations without colliding. Decisional MAPF on undirected graphs can be solved in polynomial time; Several optimization MAPF variants however are NP-complete. The directed graph variant (diMAPF) is more complex, with its decisional version already being NP-complete. This paper examines the computational approximability of optimal MAPF problems (i.e., minimizing makespan for agent travel distance and maximizing the total number of agents reaching their goals), providing a first set of several inapproximability results for these problems. The results reveal an inherent limitation in approximating optimal solutions for MAPFs, provide a deeper understanding regarding their computational intractability, thus offer foundational references for future research.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i25.34873
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Inapproximability of Optimal Multi-Agent Pathfinding Problems

Alban Grastien, Xing Tan
Proceedings of the AAAI Conference on Artificial Intelligence
Robotic Path Planning Algorithms
article

Inapproximability of Optimal Multi-Agent Pathfinding Problems

Alban Grastien, Xing Tan
article en

Abstract

Multi-agent pathfinding MAPF is a problem where multiple autonomous agents must find paths to their respective destinations without colliding. Decisional MAPF on undirected graphs can be solved in polynomial time; Several optimization MAPF variants however are NP-complete. The directed graph variant (diMAPF) is more complex, with its decisional version already being NP-complete. This paper examines the computational approximability of optimal MAPF problems (i.e., minimizing makespan for agent travel distance and maximizing the total number of agents reaching their goals), providing a first set of several inapproximability results for these problems. The results reveal an inherent limitation in approximating optimal solutions for MAPFs, provide a deeper understanding regarding their computational intractability, thus offer foundational references for future research.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(25)
Lakehead University (CA)
Lakehead University, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 90%
Robotic Path Planning Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.