Comparing Strategic Secrecy and Stackelberg Commitment in Security Games
Authors: Qingyu Guo, Bo An, Branislav Bošanský, Christopher Kiekintveld
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental evaluation shows that both strategic secrecy and Stackelberg commitment are critical measures in security domain, and our approaches can efficiently solve PBEs for realistic-sized problems. |
| Researcher Affiliation | Academia | 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, NTU, Singapore 2School of Computer Science and Engineering, Nanyang Technological University, Singapore 3Agent Technology Center, Faculty of Electrical Engineering, Czech Technical University in Prague 4Department of Computer Science, Aarhus University 5Computer Science Department, University of Texas at El Paso |
| Pseudocode | No | The paper presents mathematical formulations for MILP problems ((3) and (4)) that describe the optimization objectives and constraints, but it does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks with step-by-step procedural descriptions. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The game instances are generated as follows unless otherwise specified: each type θ is randomly drawn from { 0.1|T| , 0.1|T| + 1, .., 0.4|T| }. The probability distribution over Θ is randomly generated. The attacker s payoffs Ra t and P a t are randomly drawn from the intervals [1, 10] and [ 10, 1] respectively. The defender s payoffs are generated as follows: Rd t = ω( P a t ) + (1 ω) Rd and P d t = ω( Ra t ) + (1 ω) P d, where Rd and P d are randomly drawn from same intervals as Ra t and P a t respectively. |
| Dataset Splits | No | The paper describes generating game instances for experimental evaluation, but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | We use CPLEX for all optimizations on a 64-bit PC with 16 GB RAM and a quad-core 3.4 GHz processor. |
| Software Dependencies | No | The paper mentions using 'CPLEX for all optimizations' but does not specify a version number for CPLEX or any other key software dependencies. |
| Experiment Setup | Yes | The game instances are generated as follows unless otherwise specified: each type θ is randomly drawn from { 0.1|T| , 0.1|T| + 1, .., 0.4|T| }. The probability distribution over Θ is randomly generated. The attacker s payoffs Ra t and P a t are randomly drawn from the intervals [1, 10] and [ 10, 1] respectively. The defender s payoffs are generated as follows: Rd t = ω( P a t ) + (1 ω) Rd and P d t = ω( Ra t ) + (1 ω) P d, where Rd and P d are randomly drawn from same intervals as Ra t and P a t respectively. ... We test on random game instances with 20 targets, 8 types Θ = {θ1 = 1, .., θ8 = 8}, and varying value of ω {0.8, 0.85, 0.9, 0.95, 1.0}. |