Subgame Solving in Adversarial Team Games
Authors: Brian Zhang, Luca Carminati, Federico Cacciamani, Gabriele Farina, Pierriccardo Olivieri, Nicola Gatti, Tuomas Sandholm
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our method to a standard test suite, and we empirically show the performance improvement of the strategies thanks to subgame solving. |
| Researcher Affiliation | Collaboration | Brian Hu Zhang Computer Science Department Carnegie Mellon University bhzhang@cs.cmu.edu Luca Carminati DEIB, Politecnico di Milano luca.carminat@polimi.it Federico Cacciamani DEIB, Politecnico di Milano federico.cacciamani@polimi.it Gabriele Farina Computer Science Department Carnegie Mellon University gfarina@cs.cmu.edu Pierriccardo Olivieri DEIB, Politecnico di Milano pierriccardo.oliveri@polimi.it Nicola Gatti DEIB, Politecnico di Milano nicola.gatti@polimi.it Tuomas Sandholm Computer Science Department, CMU Strategic Machine, Inc. Strategy Robot, Inc. Optimized Markets, Inc. sandholm@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Maxmargin subgame solving with column generation, at public state P |
| Open Source Code | No | The paper states in its self-assessment checklist that code is included, but the provided text does not contain a direct link to a code repository, nor an explicit statement in the main body or appendices (other than results in Appendix C) indicating where the code for the described methodology can be accessed. |
| Open Datasets | No | The paper uses "parametric versions of the ATG instances customarily adopted in the literature" such as Kuhn poker, Leduc poker, Liar's Dice, and Tricks, citing papers [11, 18, 13, 21] for their rules. These are game definitions/frameworks, not publicly available datasets in the traditional sense with specific access information (link, DOI, repository) for data files. |
| Dataset Splits | No | The paper does not specify traditional training/validation/test dataset splits. It describes using game instances and parameters for those games, rather than data splits. |
| Hardware Specification | Yes | Each experiment was allocated 32 CPU cores and 256 GB RAM on a cluster machine. |
| Software Dependencies | Yes | Integer and linear programs were solved with Gurobi 9.5. |
| Experiment Setup | Yes | More precisely, the blueprint computation is stopped once 10 minutes have elapsed or column generation has achieved a Nash gap of /10, where is the difference between maximum and minimum team s payoffs, whichever comes first. We use a range of time limits for the strategy refinement, defined as the average time needed by a single iteration of the CG algorithm at the root of the whole game multiplied by a number α {0, 1, ..., 10}. |