Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding
Authors: Keisuke Okumura, Manao Machida, Xavier Défago, Yasumasa Tamura
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section assesses the effectiveness of PIBT quantitatively, through computer simulation. The simulator was developed in C++, and all experiments were run on a laptop with Intel Core i7 2.2GHz CPU and 8GB RAM. The experiments address both the MAPF and MAPD problems. |
| Researcher Affiliation | Collaboration | Keisuke Okumura1 , Manao Machida2 , Xavier D efago1 and Yasumasa Tamura1 1Tokyo Institute of Technology 2NEC Corporation |
| Pseudocode | Yes | Algorithm 1 describes PIBT, where pi(t) R is the priority of agent ai at timestep t. |
| Open Source Code | Yes | The code is available at https://kei18.github.io/pibt/ |
| Open Datasets | Yes | We carefully chose four undirected graphs, shown in Table 1, as testbeds for MAPF. Three of them (lak105d, arena, ost003d) come from the game Dragon Age: Origins [Sturtevant, 2012] and are commonly used to evaluate multi-agent algorithms. |
| Dataset Splits | No | The paper does not explicitly describe training/validation/test dataset splits with percentages, counts, or specific predefined split citations. It mentions using '100 settings with random initial positions and goal locations' and generating 'a sequence of 500 tasks by randomly choosing their pickup and delivery locations'. |
| Hardware Specification | Yes | The simulator was developed in C++, and all experiments were run on a laptop with Intel Core i7 2.2GHz CPU and 8GB RAM. |
| Software Dependencies | No | The paper states 'The simulator was developed in C++' but does not provide specific version numbers for any compilers, libraries, or other software dependencies. |
| Experiment Setup | Yes | The window size of WHCA was set to 5 on the simple map, and to 10 otherwise. [...] We generated a sequence of 500 tasks by randomly choosing their pickup and delivery locations from all task endpoints. We used 6 different task frequencies, which numbers of tasks are added to the task set Γ: 0.2 (one task every 5 timestep), 0.5, 1, 2, 5 and 10 where the number of agents increases from 10 to 50. |