Security Games on a Plane
Authors: Jiarui Gan, Bo An, Yevgeniy Vorobeychik, Brian Gauch
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate the value of considering SGP and effectiveness of our algorithms. and We experimentally evaluate the proposed model and the algorithms. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798 2Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235 |
| Pseudocode | No | The paper describes algorithms and methods using mathematical formulations and textual descriptions but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about open-sourcing the code or provide any links to a code repository. |
| Open Datasets | No | In the experiments, target coordinates are randomly uniformly generated in [0, n/ρ] for different target densities ρ. Player payoffs are randomly uniformly generated in [0, 1]. The data is generated, and no public dataset is cited or linked. |
| Dataset Splits | No | The paper mentions generating target coordinates and player payoffs but does not specify any dataset split information (e.g., train/validation/test percentages or counts) nor does it refer to predefined splits. |
| Hardware Specification | Yes | All results are obtained on a platform with a 3.2 GHz CPU and 16 GB memory. |
| Software Dependencies | Yes | All LPs and MILPs are solved using the existing solver CPLEX (version 12.4). |
| Experiment Setup | Yes | The result is obtained with l = 10 in the PTAS, which guarantees an approximation ratio of 80%. The parameter θ defines the variance of the players payoffs over different targets... |