Multi-Agent Corridor Generating Algorithm
Authors: Arseni Pertzovskiy, Roni Stern, Roie Zivan, Ariel Felner
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally that MACGA and MACGA+PIBT outperform baseline algorithms in terms of success rate, runtime, and makespan across diverse MAPF benchmark grids. [...] Lastly, we conducted a large set of experiments on standard MAPF benchmarks [Stern et al., 2019] comparing MACGA and MACGA+PIBT to other standard and state-ofthe-art suboptimal algorithms, namely Pr P, PIBT, LNS2, La CAM, and La CAM . The results show that MACGA and MACGA+PIBT can generate outstanding results in terms of success rate in many MAPF benchmarks. |
| Researcher Affiliation | Academia | Arseniy Pertzovsky , Roni Stern , Roie Zivan and Ariel Felner Ben-Gurion University of the Negev EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Pseudocode The high-level pseudocode of MACGA (excluding the blue text) and MACGA+PIBT (including the blue text) is illustrated in Algorithm 1. [...] Algorithm 1 MACGA +PIBT |
| Open Source Code | No | The paper states: "All algorithms were implemented in Python" but does not provide any specific links or explicit statements about releasing their source code for the methodology described. |
| Open Datasets | Yes | All experiments were performed on six different maps from the MAPF benchmark [Stern et al., 2019]: empty-32-32, random-32-32-10, random-32-32-20, room-32-32-4, maze32-32-2, and maze-32-32-4 as they present different levels of difficulty. The maps are visualized in Figure 4. |
| Dataset Splits | No | The paper states, "We executed 20 random instances per every number of agents, map, and algorithm." This describes the experimental setup for evaluation, but not specific training/test/validation splits for a dataset in the machine learning sense. |
| Hardware Specification | Yes | All algorithms were implemented in Python and ran on a Mac Book Air with an Apple M1 chip and 8GB of RAM. |
| Software Dependencies | No | The paper states "All algorithms were implemented in Python" but does not provide a specific version number for Python or any other software libraries or solvers used in the implementation. |
| Experiment Setup | Yes | The number of agents used in our experiments varied from 100 to 700. We executed 20 random instances per every number of agents, map, and algorithm. A time limit of 30 seconds was imposed on every instance. |