Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
Authors: Youzhi Zhang, Bo An
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experimental results show that CISGT is orders of magnitude faster than ISGT and the state-of-the-art algorithm to compute TMEs in large games. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore. |
| Pseudocode | Yes | Algorithm 1 ISG for a TME (ISGT) and Algorithm 2 The CTME Based ISGT (CISGT) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We conduct experiments on the classic network security games (Washburn & Wood, 1995; Jain et al., 2011; Iwashita et al., 2016)... All networks are generated by the grid model with random edges (Peng et al., 2014)... |
| Dataset Splits | No | The paper mentions generating random instances for experiments but does not explicitly detail training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | Yes | All experiments are run on a machine with 6-core 3.6GHz CPU and 32GB memory. |
| Software Dependencies | Yes | We use CPLEX solver (version 12.9) for solving all linear programs. |
| Experiment Setup | Yes | We conduct experiments on the classic network security games... (i.e., ϵ-TME with ϵ = 0.05)... By default, n = 3, and results are all averaged over 30 instances that are randomly generated. |