Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Coordinating Human-UAV Teams in Disaster Response
Authors: Feng Wu, Sarvapali D. Ramchurn, Xiaoping Chen
IJCAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,985 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,150 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL, EMAIL |
| 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. |