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..
Partial Adversarial Behavior Deception in Security Games
Authors: Thanh H. Nguyen, Arunesh Sinha, He He
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a comprehensive set of experiments, showing a significant benefit for the attacker and loss for the defender due to attacker deception. and 6 Experiments We analyze the impact of the attacker deception on: (i) the deceptive attacker s utility benefit; (ii) the defender s utility loss; and (iii) the defender s learning outcome. |
| Researcher Affiliation | Academia | Thanh H. Nguyen1 , Arunesh Sinha2 , He He1 1University of Oregon 2Singapore Management University |
| Pseudocode | No | The paper describes algorithms (GOSAQ, GAMBO) but does not provide them in a structured pseudocode block or clearly labeled algorithm figure. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | We use the game generator, GAMUT (http://gamut.stanford/edu) to generate player payoffs. In creating the training dataset, for each game, we generate M = 5 different defense strategies uniformly at random. For each generated strategy xm, we sample 50 attacks (i.e., P i nm i = 50) for the boundedly rational attacker with respect to its λ. The paper does not provide a specific link, DOI, repository name, or formal citation for a publicly available or open dataset that was used. GAMUT is a generator, not a dataset itself. |
| Dataset Splits | No | The paper describes creating a 'training dataset' (In creating the training dataset, for each game, we generate M = 5 different defense strategies uniformly at random. For each generated strategy xm, we sample 50 attacks (i.e., P i nm i = 50) for the boundedly rational attacker with respect to its λ.), but it does not specify any training/validation/test dataset splits needed for reproducibility (e.g., percentages, absolute counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run experiments, such as CPU/GPU models, memory, or cloud instances. |
| Software Dependencies | No | The paper mentions using 'GAMUT' as a game generator but does not specify its version number or any other software libraries, frameworks, or solvers with their respective version numbers. |
| Experiment Setup | Yes | In creating the training dataset, for each game, we generate M = 5 different defense strategies uniformly at random. For each generated strategy xm, we sample 50 attacks (i.e., P i nm i = 50) for the boundedly rational attacker with respect to its λ. We plot the experiment results in three cases (the xaxis in the plotted figures): (i) varying the λ of the boundedly rational attacker; (ii) varying the percentage of deceptive attacks ( f f+1); and (iii) varying resource-target ratio ( K T ). |