Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
ME-MCTS: Online Generalization by Combining Multiple Value Estimators
Authors: Hendrik Baier, Michael Kaisers
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 15,487 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,179 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with three abstractors in four board games show significant improvements of ME-MCTS over MCTS using only a single abstractor, both for MCTS with random rollouts as well as for MCTS with static evaluation functions. |
| Researcher Affiliation | Academia | Hendrik Baier , Michael Kaisers Centrum Wiskunde & Informatica, Amsterdam EMAIL |
| Pseudocode | No | The paper describes methods using equations but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper tests ME-MCTS in four different domains: Breakthrough, Knightthrough, Othello, and Rolit. However, it does not provide concrete access information for a publicly available or open dataset for these games in a formal dataset sense (e.g., no link, DOI, or specific citation to a dataset repository). |
| Dataset Splits | No | The paper does not provide specific dataset split information needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | All experiments allowed for 250 ms per move, in order to fairly expose the tradeoff between improving search with multiple estimators and the additional computational overhead of computing and combining abstractors. The implemented abstractors each have two parameters: a bias parameter that is used to compute their influence on every move choice, and an exploration factor for the exploration-exploitation tradeoff of the AUER algorithm. Vanilla MCTS has one parameter: the exploration factor of UCB1. |