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..
Improving Search with Supervised Learning in Trick-Based Card Games
Authors: Christopher Solinas, Douglas Rebstock, Michael Buro1158-1165
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We use two methods of measuring inference performance in this work. First, we measure the quality of our inference technique in isolation using a novel metric. Second, we show the effect of using inference in a card player by running tournaments against several baseline players. |
| Researcher Affiliation | Academia | Christopher Solinas, Douglas Rebstock, Michael Buro Department of Computing Science, University of Alberta Edmonton, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1: PIMC with state inference |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing their source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | The networks are trained using a total of 20 million games played by humans on a popular Skat server (DOSKV 2018). |
| Dataset Splits | No | The paper mentions using a 'validation set' for early stopping but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments, only vague terms like 'modern hardware'. |
| Software Dependencies | No | The paper mentions using 'Python Tensorflow (Abadi et al. 2016)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Table 1 lists all hyperparameters used during training. Parameter Value Dropout 0.8 Batch Size 32 Optimizer ADAM Learning Rate (LR) 10^-4 LR Exponential Decay 0.96 / 10,000,000 batches |