BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares
Authors: Karo Castro-Wunsch, William Maga, Calin Anton
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigated Parameterized Poker Squares to approximate an optimal game playing agent. We organized our inquiry along three dimensions: partial hand representation, search algorithms, and partial hand utility learning. For each dimension we implemented and evaluated several designs, among which we selected the best strategies to use for Bee Mo, our final product. |
| Researcher Affiliation | Academia | Karo Castro-Wunsch, William Maga, Calin Anton Mac Ewan University, Edmonton, Alberta, Canada karoantonio@gmail.com, magaw@mymacewan.ca, antonc@macewan.ca |
| Pseudocode | No | The paper describes algorithms in prose and uses a flowchart (Figure 1), but does not present structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | To facilitate the reproducibility of our results and to encourage further work on Parameterized Poker Squares, we made the Java source code of the agent available at: https://github.com/Mr Magaw/Beemo. |
| Open Datasets | No | The paper describes an agent playing Parameterized Poker Squares and mentions different scoring systems (e.g., American, British), but does not refer to a publicly available dataset or provide access information for data used for training the agent. |
| Dataset Splits | No | The paper mentions 'training phase' and 'evaluation phase' for learning hand pattern utilities, stating 'In the training phase, 5,000 simulated games are played... At the end of a simulated game, each pattern utility is computed as the average of the scores of complete hands that resulted from hands which matched the pattern.' and 'In the evaluation phase 5,000 games are played'. However, these refer to simulated games for learning utilities, not a standard validation split on a fixed dataset. |
| Hardware Specification | No | Bee Mo takes full advantage of multi core processors, by creating several threads for game simulations, which are then run in parallel on the available cores. |
| Software Dependencies | No | The paper mentions 'Java implementation' and provides a GitHub link for the Java source code, but does not specify any software dependencies with version numbers (e.g., 'Java 7', 'Java Development Kit 8', or specific library versions). |
| Experiment Setup | Yes | For MC search variations 100 games were simulated at each node. |