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 | Conference PDF | Archive PDF | Plain Text | 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, {qguo005, boan}@ntu.edu.sg 2Department of Computer Science, University of Oxford, jiarui.gan@cs.ox.ac.uk 3School of Computer Science, Carnegie Mellon University, feifang@cmu.edu 4Department of Electronics and Computer Science, University of Southampton, ltt08r@ecs.soton.ac.uk 5Center for Artificial Intelligence in Society, University of Southern California, tambe@usc.edu |
| 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}. |