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..
Agile Planning for Real-World Disaster Response
Authors: Feng Wu, Sarvapali D. Ramchurn, Wenchao Jiang, Jeol E. Fischer, Tom Rodden, Nicholas R. Jennings
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our algorithm and show that it outperforms current benchmarks. Our algorithm is also shown to perform better in pilot studies with real humans. |
| Researcher Affiliation | Academia | Feng Wu* Sarvapali D. Ramchurn Wenchao Jiang Jeol E. Fischer Tom Rodden Nicholas R. Jennings *Computer Science and Technology, University of Science and Technology of China, Hefei, China Electronics and Computer Science, University of Southampton, Southampton, UK Mixed Reality Lab, University of Nottingham, Nottingham, UK |
| Pseudocode | Yes | Algorithm 1: Two-Pass UCT Planning |
| Open Source Code | No | A video of our pilot runs can be viewed at: http://bit.ly/1eb NYty. This link leads to a video demonstration, not source code for the methodology. |
| Open Datasets | No | We built an MMDP simulator for this scenario. The paper describes a custom simulation environment but does not mention making the dataset or simulation data publicly available. |
| Dataset Splits | No | The paper mentions running simulations and pilot studies but does not specify details on train/validation/test splits, percentages, or explicit sample counts for dataset partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions building an MMDP simulator and using UCT, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In the experiments, we initialize the rejection model by randomly generating the preference of each FR and set the discount factor γ = 0.95, the rejection limit k = 3, and the rejection cost C = 1.0. |