Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 Arti๏ฌcial 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 ฯ0 = maxi T Ra i 4(Rd i P d i ) which gives an additive 1 2-approximate solution. |