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..
Deception in Finitely Repeated Security Games
Authors: Thanh H. Nguyen, Yongzhao Wang, Arunesh Sinha, Michael P. Wellman2133-2140
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational experiments illuminate conditions conducive to strategic deception, and quantify bene๏ฌts to the attacker. Finally, we present a detailed experimental analysis of strategic deception, showing how various game factors affect the tendency for the attacker to deviate from myopic best responses to mislead the defender. |
| Researcher Affiliation | Academia | 1University of Oregon, EMAIL 2University of Michigan, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | In our experiments, the players rewards and penalties are generated uniformly at random in the range [1, 10] and [ -10, -1] respectively. |
| Dataset Splits | No | The paper describes generating game instances randomly for experiments but does not provide specific dataset split information (e.g., train/validation/test percentages or counts) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In our experiments, the players rewards and penalties are generated uniformly at random in the range [1, 10] and [ -10, -1] respectively. We analyze games with number of attacker types: |ฮ| = 2, number of targets: |N| {4, 6, 8, 10, 12}, and number of time steps |T| {2, 3}. In our third experiment, we vary the number of defender resources in 2-step games with 2 attacker types. |