Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Security Games on a Plane
Authors: Jiarui Gan, Bo An, Yevgeniy Vorobeychik, Brian Gauch
AAAI 2017 | Venue PDF | 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/ฯ] for different target densities ฯ. 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 ฮธ de๏ฌnes the variance of the players payoffs over different targets... |