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..
Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty
Authors: Thanh Nguyen, Amulya Yadav, Bo An, Milind Tambe, Craig Boutilier
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the effectiveness of our approaches. |
| Researcher Affiliation | Academia | 1University of Southern California, Los Angeles, CA 90089 EMAIL 2Nanyang Technological University, Singapore 639798 EMAIL 3University of Toronto, Canada M5S 3H5 EMAIL |
| Pseudocode | Yes | Algorithm 1: Constraint-generation (MIRAGE) |
| Open Source Code | No | The paper does not provide concrete access to its source code, nor does it explicitly state that the code is available. |
| Open Datasets | No | The paper states that games were 'generated using GAMUT' and describes the generation process, but does not refer to or provide access information for a publicly available or open dataset in the traditional sense of a pre-existing collection of data. |
| Dataset Splits | No | The paper describes how game instances were randomly generated and evaluated, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were run on a 2.83GHz Intel processor with 4GB of RAM |
| Software Dependencies | Yes | using CPLEX 12.3 for LP/MILPs and KNITRO 8.0.0.z for nonlinear optimization. |
| Experiment Setup | Yes | Upper and lower bounds for payoff intervals are generated randomly from [ 14, 1] for penalties and [1, 14] for rewards, with the difference between the upper and lower bound (i.e., interval size) exactly 2 (this gives payoff uncertainty of roughly 30%). All results are averaged over 120 instances (20 games per covariance value) and use eight defender resources unless otherwise specified. |