Solving Zero-Sum Security Games in Discretized Spatio-Temporal Domains
Authors: Haifeng Xu, Fei Fang, Albert Jiang, Vincent Conitzer, Shaddin Dughmi, Milind Tambe
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results based on a ferry-protection domain show that our algorithms scale-up significantly beyond what is achievable by Fang, Jiang, and Tambe (2013). We test our algorithms in both practical settings in the ferry protection domain and randomly generated settings. Results are shown in Figure 6 and 7. |
| Researcher Affiliation | Academia | 1University of Southern California, Los Angeles, CA 90007, USA {haifengx,feifang,jiangx,shaddin,tambe}@usc.edu 2Duke University, Durham, NC 27708, USA conitzer@cs.duke.edu |
| Pseudocode | Yes | Algorithm 1 Dynamic Programming for Weight Collection Algorithm 2 Greedy Algorithm for Deterministic Coverage Problem (DC) Algorithm 3 Greedy Weight Coverage |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | No | The paper mentions using a "ferry protection domain" and "randomly generated settings" but does not provide specific access information (link, DOI, citation with authors/year, or repository) for the datasets used to allow public access or reproduction. |
| Dataset Splits | No | The paper does not specify exact percentages or sample counts for training, validation, or test dataset splits. It mentions calculating average Att EU ratio over "20 sampled instances" but this refers to experiment runs, not data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using "linear programming formulation" and implementing algorithms using "column generation" but does not provide specific software names with version numbers (e.g., particular LP solvers, programming languages, or libraries with their versions). |
| Experiment Setup | No | Figure 5 lists "Main Parameters" which are problem domain parameters (M, N, K, T, delta_m, R_e, Utility Range) that were varied for experiments, not specific computational training setup details or hyperparameters of the algorithms themselves (e.g., learning rates, batch sizes, optimizers). |