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..
Computing Quantal Stackelberg Equilibrium in Extensive-Form Games
Authors: Jakub Černý, Viliam Lisý, Branislav Bošanský, Bo An5260-5268
AAAI 2021 | Venue PDF | 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. |