Bounded Suboptimal Multi-Agent Path Finding Using Highways
Authors: Liron Cohen, Sven Koenig
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate experimentally that ECBS+HWY outperforms ECBS (in terms of runtime and solution cost) in Kiva-like domains with many agents if the highway s layout captures the domain structure well (that is, the highway edges are such that agents have incentives to move in only one direction in each corridor). Figures 3 and 4 show some experimental results. |
| Researcher Affiliation | Academia | Liron Cohen and Sven Koenig Department of Computer Science, University of Southern California {lironcoh,skoenig}@usc.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes generating instances on a 'Kiva-like domain' model (Figure 2) but does not provide access information (link, DOI, specific citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating instances and running experiments over '10 trials' but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Figure 2: Our model of a Kiva-like domain. Highway edges are represented by red arrows. For a given number of agents, we generate instances by assigning half of the agents a randomly chosen start location in Area1 and a randomly chosen goal location in Area2, and vice-versa for the other half. Figure 3: Each data point is the median runtime over 10 trials. Figure 4: Each data point is an instance with 150 agents solved within 5 minutes. |