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..
Refining Subgames in Large Imperfect Information Games
Authors: Matej Moravcik, Martin Schmid, Karel Ha, Milan Hladik, Stephen Gaukrodger
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our technique using one of the top participants of the AAAI-14 Computer Poker Competition, the leading playground for agents in imperfect information settings. For the first time, we evaluate these techniques on the safe-refinement task as part of a large-scale game by using one of the top participating agents in AAAI-14 Computer Poker Competition as the baseline strategy to be refined in subgames. |
| Researcher Affiliation | Collaboration | Matej Moravcik, Martin Schmid, Karel Ha, Milan Hladik Charles University In Prague {moravcim, schmidm, karelha, hladik} @kam.mff.cuni.cz, Stephen J. Gaukrodger Koypetition EMAIL |
| Pseudocode | No | The paper describes steps for its gadget construction but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper mentions using a participant of the 'AAAI-14 Computer Poker Competition' and 'heads-up no-limit Texas Hold em Total Bankroll' as the game environment, but it does not specify a publicly available dataset with concrete access information (link, DOI, formal citation) in the traditional sense of a machine learning dataset. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits (percentages, sample counts, or explicit references to predefined splits). |
| Hardware Specification | No | The paper mentions 'Computational resources were provided by the Meta Centrum under the program LM2010005 and the CERIT-SC under the program Centre CERIT Scientific Cloud, part of the Operational Program Research and Development for Innovations, Reg. no. CZ.1.05/3.2.00/08.0144.' This indicates the use of computational resources but does not provide specific hardware details like CPU or GPU models. |
| Software Dependencies | No | The paper mentions 'CFR+' and 'domain-specific speedup tricks' but does not provide specific software names with version numbers for reproducibility (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We ran 10, 000 iterations of the CFR+ algorithm in the corresponding gadget games. Exponential weighting is used to update the average strategies (Tammelin et al. 2015). Each technique was used to refine around 2, 000 subgames. |