Inapproximability of Optimal Multi-Agent Pathfinding Problems
Authors: Xing Tan, Alban Grastien
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Lakehead University, Canada 2Universit e Paris-Saclay, CEA, List, F-91120, Palaiseau, France EMAIL, EMAIL |
| Pseudocode | No | The paper describes reductions and proofs for theoretical complexity analysis but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about making source code available, nor does it provide links to code repositories. |
| Open Datasets | No | The paper focuses on theoretical problem instances (like 3DM and MAXE3SAT) used for reductions, not empirical datasets. No datasets are mentioned as being publicly available with access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets that would require training/test/validation splits. |
| Hardware Specification | No | The paper focuses on theoretical complexity analysis and does not describe any experimental setup that would involve specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software or library dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not present empirical experiments, therefore, there is no experimental setup, hyperparameters, or system-level training settings described. |