Computing Quantal Stackelberg Equilibrium in Extensive-Form Games
Authors: Jakub Černý, Viliam Lisý, Branislav Bošanský, Bo An5260-5268
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate that our algorithm provides higher quality results several orders of magnitude faster than a baseline method for general non-linear optimization. |
| Researcher Affiliation | Academia | 1 Nanyang Technological University, Singapore 2 AI Center, FEE, Czech Technical University in Prague, Czech Republic |
| Pseudocode | Yes | Algorithm 1: Dinkelbach-Type Algorithm for QSE |
| Open Source Code | No | The paper cites an appendix for full proofs and additional examples ('Cerny et al. 2021. Computing Quantal Stackelberg Equilibrium in Extensive Form Games: Appendix. https://cloud.disroot.org/s/ 4Cin5Ny3nm Zz Wk R. Accessed: 2021-03-15.'), but this link points to supplementary paper material (PDFs), not source code for the methodology. |
| Open Datasets | No | The paper uses 'Search Game' and 'Network Game' domains where instances are constructed based on described rules and random parameters, rather than utilizing pre-existing, publicly available datasets with concrete access information (links, DOIs, or formal citations). |
| Dataset Splits | No | The paper describes how game instances are generated and specifies algorithm parameters ('The tolerance parameter for the COBYLA algorithm in NLOPT was set to 10 2 and ϵB = 1% of the leader s utility range for the DTA s binary search. The linearization uses K = 3, the basis of MDT is set to b = 3 and the size of the precision interval E is L = 4.'), but it does not specify train/validation/test splits for any dataset, as the experiments involve generated game instances. |
| Hardware Specification | Yes | The experiments were performed on a 3.2GHz CPU with 16GB RAM. |
| Software Dependencies | Yes | All implementations were done in C++17. We used NLOPT 2.6.1, and a single-threaded IBM CPLEX 12.8 carried all MILP computations. |
| Experiment Setup | Yes | The tolerance parameter for the COBYLA algorithm in NLOPT was set to 10 2 and ϵB = 1% of the leader s utility range for the DTA s binary search. The linearization uses K = 3, the basis of MDT is set to b = 3 and the size of the precision interval E is L = 4. |