Coordinating Human-UAV Teams in Disaster Response

Authors: Feng Wu, Sarvapali D. Ramchurn, Xiaoping Chen

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Our empirical results confirm that our algorithm significantly outperforms the state-of-the-art both in time and solution quality. We empirically evaluated our algorithm using a benchmark simulator for disaster response and show that our algorithm outperforms the leading POMDP solver (i.e., POMCP) in the bench-mark domain with faster runtime and better solution quality.
Researcher Affiliation Academia Feng Wu Sarvapali D. Ramchurn Xiaoping Chen Computer Science and Technology, University of Science and Technology of China, Hefei, China Electronics and Computer Science, University of Southampton, Southampton, UK wufeng02@ustc.edu.cn, sdr1@soton.ac.uk, xpchen@ustc.edu.cn
Pseudocode Yes Algorithm 1: Simulation-Based Task Planning
Open Source Code No No statement regarding the release of open-source code or a link to a code repository was found.
Open Datasets Yes To evaluate our algorithm, we extended an existing benchmark simulator used to develop prototypes for real-world studies [Ramchurn et al., 2015c].
Dataset Splits No The paper does not explicitly provide details about training/test/validation dataset splits (e.g., percentages, sample counts, or specific split files).
Hardware Specification Yes A machine with a 3.50GHz Intel Core i7 CPU and 8GB RAM was used to produce the results.
Software Dependencies No The paper does not provide specific software dependencies with version numbers used for its implementation.
Experiment Setup No The paper mentions varying the number of simulations and running algorithms 1000 times, but does not provide specific hyperparameter values or detailed training configurations for the experimental setup.