Split Moves for Monte-Carlo Tree Search
Authors: Jakub Kowalski, Maksymilian Mika, Wojciech Pawlik, Jakub Sutowicz, Marek Szykuła, Mark H. M. Winands10247-10255
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. |
| Researcher Affiliation | Academia | 1University of Wrocław, Faculty of Mathematics and Computer Science 2Maastricht University, Department of Data Science and Knowledge Engineering |
| Pseudocode | Yes | Algorithm 1: Vanilla semisplit MCTS. |
| Open Source Code | Yes | The full version of this paper is available at (Kowalski et al. 2021b), and the source code used for the experiments is shared within the RBG implementation (Kowalski et al. 2021a). |
| Open Datasets | Yes | Our test set consists of 12 board games, well known in GGP (Amazons, Breakthrough, Breakthru, Chess, Chess without check, English Draughts, Fox And Hounds, Go, Knightthrough, Pentago, Skirmish, and The Mill Game). |
| Dataset Splits | No | The paper describes testing agents against baselines in timed and fixed settings but does not mention explicit train/validation/test dataset splits like percentages or sample counts for data. |
| Hardware Specification | No | The computations were run on a computational grid in the Institute of Computer Science, University of Wrocław, funded by National Science Centre, Poland, under project number 2019/35/B/ST6/04379. (This is a general description of the computing environment, but it lacks specific hardware details like GPU/CPU models, memory, etc.) |
| Software Dependencies | No | The paper mentions 'RBG implementation (Kowalski et al. 2021a)' but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | All parameters (e.g., the exploration factor, MAST and RAVE policies) were set according to the recommendations in the literature (Finnsson and Björnsson 2010; Sironi and Winands 2016) and keep the same for every agent. (The paper refers to recommendations in literature for parameter settings, but does not state the specific parameter values within the text.) |