Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
Authors: Reid McIlroy-Young, Yu Wang, Siddhartha Sen, Jon Kleinberg, Ashton Anderson
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a transformer-based approach to behavioral stylometry in the context of chess, where one attempts to identify the player who played a set of games. Our method operates in a few-shot classification framework, and can correctly identify a player from among thousands of candidate players with 98% accuracy given only 100 labeled games. |
| Researcher Affiliation | Collaboration | Reid Mc Ilroy-Young University of Toronto reidmcy@cs.toronto.edu Russell Wang University of Toronto russell@cs.toronto.edu Siddhartha Sen Microsoft Research sidsen@microsoft.com Jon Kleinberg Cornell University kleinberg@cornell.edu Ashton Anderson University of Toronto ashton@cs.toronto.edu |
| Pseudocode | No | The paper describes its methodology in detail through text and diagrams (Figure 1, Figure 2, Figure 3), but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | Our supplement contains the code, the model files are too large for the supplement so will be released when the paper goes public |
| Open Datasets | Yes | We use chess games downloaded from Lichess, a popular open-source chess platform. Their game database contains over two billion games played by players ranging from beginners to the current world champion, Magnus Carlsen. |
| Dataset Splits | Yes | Finally, each player s games are randomly split into training games (80% of their games), reference games (10%) , and query games (10%). |
| Hardware Specification | Yes | The entire model was trained with 4 Tesla K80 GPUs |
| Software Dependencies | No | The paper mentions using a transformer and specific architectures (e.g., Vision Transformer), but it does not provide version numbers for any software dependencies like Python, PyTorch, TensorFlow, or specific libraries. |
| Experiment Setup | Yes | We randomly sample N M games as a batch, where N = 40 is number of players and M = 20 is number of games per player. In order to speed up training and perform batch computing, we randomly sample a 32-move sequence from each game and pad 0s to games with length less than 32 moves. The entire model was trained with 4 Tesla K80 GPUs and SGD optimizer with an initial learning rate of 0.01 and momentum of 0.9. The learning rate was reduced by half every 40K steps. Initial values of w and b for the similarity matrix were chosen to be (w, b) = (10, 5), and we used a smaller gradient scale of 0.01 to match the original GE2E model. |