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 [1].
On the Inducibility of Stackelberg Equilibrium for Security Games
Authors: Qingyu Guo, Jiarui Gan, Fei Fang, Long Tran-Thanh, Milind Tambe, Bo An2020-2028
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our solution concept and proposed algorithmic implementation with extensive experiments. All results are obtained on a platform with a 2.60 GHz dual-core CPU and 8.0 GB memory. All linear programs are solved using the existing solver CPLEX (version 12.4). The random instances are generated as follows: rewards and penalties are all integers randomly drawn from [0, 5] and [−5, 0] respectively. Each schedule is randomly generated covering a fixed number l of targets and each target is ensured to be covered by at least one schedule. The resources are all homogeneous, i.e., Sr = S for any r ∈ R. Unless otherwise specified, all results are averaged on 100 randomly generated instances. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, EMAIL 2Department of Computer Science, University of Oxford, EMAIL 3School of Computer Science, Carnegie Mellon University, EMAIL 4Department of Electronics and Computer Science, University of Southampton, EMAIL 5Center for Artificial Intelligence in Society, University of Southern California, EMAIL |
| Pseudocode | No | The paper describes algorithmic steps in paragraph form, such as in the 'Computing an ISE' section, but it does not present formal pseudocode or algorithm blocks (e.g., Algorithm 1, Pseudocode). |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for their methodology. |
| Open Datasets | No | The random instances are generated as follows: rewards and penalties are all integers randomly drawn from [0, 5] and [−5, 0] respectively. Each schedule is randomly generated covering a fixed number l of targets and each target is ensured to be covered by at least one schedule. The resources are all homogeneous, i.e., Sr = S for any r ∈ R. |
| Dataset Splits | No | The paper describes how its random instances are generated and the parameters used for these generations, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All results are obtained on a platform with a 2.60 GHz dual-core CPU and 8.0 GB memory. |
| Software Dependencies | Yes | All linear programs are solved using the existing solver CPLEX (version 12.4). |
| Experiment Setup | Yes | The random instances are generated as follows: rewards and penalties are all integers randomly drawn from [0, 5] and [−5, 0] respectively. Each schedule is randomly generated covering a fixed number l of targets and each target is ensured to be covered by at least one schedule. The resources are all homogeneous, i.e., Sr = S for any r ∈ R. Unless otherwise specified, all results are averaged on 100 randomly generated instances. [...] The game instances are randomly generated with l = 5, |R| = 5, |T| ranges from 50 to 400 with step size of 50, and |S| = |T|/2. [...] 500 instances are randomly generated with 200 targets, 1 resource, |S| ∈ {16, 18, 20} and l ∈ {16, 18, 20}. |