Fault-Tolerant Offline Multi-Agent Path Planning
Authors: Keisuke Okumura, Sébastien Tixeuil
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |