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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Imitative Attacker Deception in Stackelberg Security Games
Authors: Thanh Nguyen, Haifeng Xu
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,705 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,660 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 experiments illustrate signi๏ฌcant defender loss due to imitative attacker deception, suggesting the potential side effect of learning from the attacker. (Abstract) 5 Experiments We evaluate the solution quality of our proposed deceptive algorithm. |
| Researcher Affiliation | Academia | 1University of Oregon 2Harvard University |
| Pseudocode | No | The paper presents mathematical formulations (MINLP) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using 'GAMUT (http://gamut.stanford.edu/)' which is a third-party tool, but does not provide any links or statements for its own source code for the methodology described. |
| Open Datasets | No | The paper states that data is 'generated... using the covariance game generator, GAMUT (http://gamut.stanford.edu/)' but does not provide concrete access (link, DOI, citation) to a publicly available or open dataset that was used for training. |
| Dataset Splits | No | The paper states 'Each data point in our results is averaged over 250 different games' but does not provide specific details on training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions the 'covariance game generator, GAMUT', but does not list any specific software or library names with version numbers that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Each data point in our results is averaged over 250 different games (50 games per covariance value). Finally, we consider two scenarios: (i) small deceptive payoff space with an interval size of I = 1.0; and (ii) large space with I = 2.0. |