Designing the Game to Play: Optimizing Payoff Structure in Security Games
Authors: Zheyuan Ryan Shi, Ziye Tang, Long Tran-Thanh, Rohit Singh, Fei Fang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evaluation: We provide extensive experimental evaluation for the proposed algorithms. For problems with L1-norm form budget constraint, we show that the branchand-bound approach with an additive approximation guaran- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) tee can solve up to hundreds of targets in a few minutes. |
| Researcher Affiliation | Academia | 1 Swarthmore College, USA 2 Carnegie Mellon University, USA 3 University of Southampton, UK 4 World Wide Fund for Nature, Cambodia |
| Pseudocode | Yes | Algorithm 1 Branch-and-bound [...] Algorithm 2 PTAS for a special case in L1 [...] Algorithm 3 Algorithm for budget in L0-norm form |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. A footnote provides a link to the arXiv version of the paper itself: "https://arxiv.org/abs/1805.01987". |
| Open Datasets | No | The original payoff structures are randomly generated integers between 1 and 2n with penalties obtained by negation (recall n is the number of targets). Budget and weights of the manipulations are randomly generated integers between 1 and 4n. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | Yes | For each problem size, we run 60 experiments on a PC with Intel Core i7 processor. |
| Software Dependencies | No | Gurobi is used for solving MILPs, which is terminated when either time limit (15 min) or optimality gap (1%) is achieved. (A version number for Gurobi is not provided). |
| Experiment Setup | Yes | Gurobi is used for solving MILPs, which is terminated when either time limit (15 min) or optimality gap (1%) is achieved. [...] We set ρ0 = maxi T Ra i 4(Rd i P d i ) which gives an additive 1 2-approximate solution. |