Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach
Authors: Hau Chan, Long Tran-Thanh, Vignesh Viswanathan
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate empirically by using a realworld dataset that Firefly achieves up to 80 90% performance of the offline optimal solution, even with a small number of drones, in most cases. Finally, we numerically evaluate the performance of Firefly in a large variety of settings. |
| Researcher Affiliation | Academia | 1University of Nebraska-Lincoln, USA 2University of Southampton, UK 3Indian Institute of Technology, Kharagpur, India |
| Pseudocode | Yes | Algorithm 1: The Firefly Algorithm; Algorithm 2: The Geommetric Resampling Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using a 'realworld dataset' in the abstract, but later describes generating data for 'realistic fire fighting instances' based on a region in Marin County, California, by drawing values from distributions. No concrete access information (link, DOI, or formal citation to a publicly available dataset) is provided. |
| Dataset Splits | No | The paper describes generating 10 problem instances for each parameter combination and reporting averages, but it does not specify any training, validation, or test dataset splits, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Given the 10 x 10 grid graph structure, we consider r {10, 50, 100, 200}, m {1, 5, 10, 50} and C {10, 50}. For each of the combinations of the parameters, we generate 10 problem instances. The reported ratio is the average over the 10 problem instances (with different zone priorities). The η value is set to be 0.5 for all of the experiments. Finally, we let T = 200 to be the maximum number of time-steps. |