Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bounded Suboptimal Multi-Agent Path Finding Using Highways
Authors: Liron Cohen, Sven Koenig
IJCAI 2016 | Venue PDF | 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 EMAIL |
| 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. |