Playing Repeated Network Interdiction Games with Semi-Bandit Feedback
Authors: Qingyu Guo, Bo An, Long Tran-Thanh
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments also show that SBGA significantly outperforms existing approaches with fast convergence rate. ... All computations were performed on a 64-bit PC with 16 GB RAM and a quad-core 3.4 GHz processor. The tested networks are random planar graphs generated by the Waxman geographical model (WG) suitable for modeling transportation networks [Waxman, 1988]. |
| 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 3Electronics and Computer Science, University of Southampton, UK |
| Pseudocode | Yes | Algorithm 1: SBGA; Algorithm 2: GEX |
| Open Source Code | No | The paper does not provide any links to open-source code or state that the code for their method is publicly available. |
| Open Datasets | No | The paper uses 'random planar graphs generated by the Waxman geographical model' and states 'All inspection stations are randomly placed on the graph and all candidate paths for the adversarial flow are randomly generated.' This indicates data generation, not the use of a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes generating and using instances for experiments but does not specify data partitioning percentages or sample counts for reproduction. |
| Hardware Specification | Yes | All computations were performed on a 64-bit PC with 16 GB RAM and a quad-core 3.4 GHz processor. |
| Software Dependencies | Yes | The submodular maximization problem of GEX subroutine in SBGA is easily formulated as a convex integer program, which is solved by KNITRO (version 9.0.0) efficiently. |
| Experiment Setup | Yes | By default, the instances are parameterized as follows: the number of nodes |N| = 200 and the average degree is 3.0. The number of inspection stations n = 100. The number of candidate paths for adversarial flow m = 20, and the number of resources k {10, 20}, corresponding with m {1, 2}. ... The total number of rounds T = 1000. The results are averaged on 100 runs on one randomly generated instance. The learning parameters in all algorithms are set to their optimal or rough optimal values w.r.t. the theoretical regret bounds. |