Multi-Agent Path Finding for Large Agents
Authors: Jiaoyang Li,Pavel Surynek,Ariel Felner,Hang Ma,T. K. Satish Kumar,Sven Koenig7627-7634
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. |
| Researcher Affiliation | Academia | Jiaoyang Li CS Department Univ. of Southern California jiaoyanl@usc.edu; Pavel Surynek Faculty of Information Technology Czech Technical University pavel.surynek@fit.cvut.cz; Ariel Felner SISE Department Ben-Gurion University felner@bgu.ac.il; Hang Ma T. K. Satish Kumar Sven Koenig CS Department Univ. of Southern California |
| Pseudocode | Yes | Algorithm 1: A template for Max Weight-d. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the methodology described. |
| Open Datasets | Yes | We used a large 4-neighbor 194 x 194 2D grid with 51.3% blocked cells, namely the benchmark game map lak503d from (Sturtevant 2012). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | Yes | All algorithms were written in C++ and ran on a 2.80 GHz Intel Core i7-7700 laptop with 8 GB RAM and a runtime limit of 5 minutes. |
| Software Dependencies | No | The paper mentions that algorithms were written in C++ and that an off-the-shelf ILP solver was used, but it does not provide specific version numbers for any software components, libraries, or solvers. |
| Experiment Setup | Yes | Each agent is a 2.5 2.5 square whose reference point is its top-left corner. All algorithms use Equation (1) to detect conflicts. All MC-CBS variants use the rectangle constraints discussed in Section 4.3. For MAX, we tested lookahead depths d from 0 to 4. We used 50 instances with randomly generated start vertices and goal vertices for each number of agents. |