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..
Fault-Tolerant Offline Multi-Agent Path Planning
Authors: Keisuke Okumura, Sébastien Tixeuil
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate DCRF with named failure detectors in grid environments and observe that DCRF can address more problem instances compared to computing a set of vertex disjoint paths... This section evaluates DCRF in both SYN and SEQ with NFD. We present a variety of aspects including merits to consider MAPPCF and bottlenecks of the planning. Figure 7 shows the results. |
| Researcher Affiliation | Academia | 1 Tokyo Institute of Technology, Japan 2 Sorbonne University, CNRS, LIP6, Institut Universitaire de France, France |
| Pseudocode | Yes | Algorithm 1 presents DCRF. |
| Open Source Code | Yes | The supplementary material is available on https://kei18.github. io/mappcf. |
| Open Datasets | Yes | Each scenario was tested on two four-connected grids (size: 32x32 and 64x64) obtained from (Stern et al. 2019). |
| Dataset Splits | No | The paper mentions preparing '25 well-formed instances' but does not specify any training, validation, or testing splits for these instances, nor does it refer to a validation set in the context of model training. |
| Hardware Specification | Yes | The experiments were run on a desktop PC with Intel Core i9-7960X 2.8 GHz CPU and 64 GB RAM. |
| Software Dependencies | No | The paper states 'The code was written in Julia', but it does not specify a version number for Julia or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | The initial paths were obtained by prioritized planning... Single-agent pathfinding was implemented by A , adding a heuristic that penalizes the use of common vertices... We applied the refinement over initial paths... The attempt is a failure if it returns a solution before the timeout of 30 s. |