Audit Games with Multiple Defender Resources
Authors: Jeremiah Blocki, Nicolas Christin, Anupam Datta, Ariel Procaccia, Arunesh Sinha
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we experimentally demonstrate that this transformation significantly speeds up computation of solutions for a class of audit games and security games. In this section, we empirically demonstrate the speedup gains from our optimization transformation for both audit games and security games. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, USA; {arielpro@cs., jblocki@cs., danupam@, nicolasc@}cmu.edu 2University of Southern California, USA; aruneshs@usc.edu |
| Pseudocode | Yes | Algorithm 1: CONSTRAINT FIND(T, R) and Algorithm 2: APX SOLVE(l, EQ(j)) |
| Open Source Code | No | The paper states 'Code was written in Matlab using the built-in large scale interior point method implementation of linear programming' but does not provide a concrete link to or statement about the public availability of their source code for the methodology described. |
| Open Datasets | No | The paper states that 'utilities were generated randomly from the range [0, 1]' for their experiments, indicating synthetic data, and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It mentions 'random utilities' and running experiments for '5 runs' but no detailed split information for reproducibility. |
| Hardware Specification | Yes | Our experiments were run on a desktop with quad core 3.2 GHz processor and 6GB RAM. |
| Software Dependencies | No | The paper states 'Code was written in Matlab using the built-in large scale interior point method implementation of linear programming.' However, it does not provide specific version numbers for Matlab or any particular libraries/solvers used. |
| Experiment Setup | Yes | We used the same problem inputs in which utilities were generated randomly from the range [0, 1], a was fixed to 0.01, x was discretized with interval size of 0.005. |