The Price of Usability: Designing Operationalizable Strategies for Security Games
Authors: Sara Marie Mc Carthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive numerical evaluation that showcases the solution quality and scalability of our approach and illustrate that the Price of Usability is typically not high. 5 Evaluation We evaluate our algorithms on several different sized instances of SORT-TSG. |
| Researcher Affiliation | Academia | 1 University of Southern California 2 University of Twente 3 University of Michigan |
| Pseudocode | Yes | Algorithm 1 outlines this process. Algorithm 2 outlines the steps of this solution method. This subroutine is outlined in Algorithm 3. This subroutine is outlined in Algorithm 4. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper states 'Each experiment is averaged over 50 randomized instances of the remaining parameters,' indicating that they generated their own data instances based on specified parameters rather than using a publicly available dataset, and no access information (link, DOI, citation) is provided for these generated instances. |
| Dataset Splits | No | The paper does not provide details on specific training, validation, or test dataset splits, nor does it mention cross-validation or other data partitioning strategies. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run its experiments, such as GPU or CPU models, memory, or cloud resources. |
| Software Dependencies | No | The paper mentions formulating problems as mixed-integer linear programs but does not provide specific names or version numbers for any software, libraries, or solvers used for their implementation or for running the experiments. |
| Experiment Setup | No | The paper describes the problem instance parameters (W, C, R) and the number of randomized instances for evaluation, but it does not provide specific experimental setup details such as hyperparameters, optimization settings, or other configuration parameters for the algorithms or models used. |