Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inapproximability of Optimal Multi-Agent Pathfinding Problems
Authors: Xing Tan, Alban Grastien
AAAI 2025 | Venue PDF | 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. |