Summary of Chess Rating Estimation From Moves and Clock Times Using a Cnn-lstm, by Michael Omori et al.
Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM
by Michael Omori, Prasad Tadepalli
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method estimates player ratings directly from game moves and clock times, aiming to overcome limitations in current chess rating systems. A CNN learns positional features, which are then integrated with clock-time data into a Bidirectional LSTM to predict player ratings after each move. The model achieved an MAE of 182 rating points on the test data. Additionally, it was applied to the 2024 IEEE Big Data Cup Chess Puzzle Difficulty Competition dataset, predicting puzzle ratings and achieving competitive results. This method is the first to use no hand-crafted features to estimate chess ratings and also the first to output a rating prediction after each move. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to calculate chess player ratings based on game moves and clock times. It’s like a super smart chess coach that can figure out how good a player is after every single move! The researchers used over one million games from Lichess to train their model, which was then tested on another big dataset. They even entered a competition where they predicted how hard puzzles would be for other players and did well. This new method is special because it doesn’t need any extra information about the game, just the moves and clock times. |
Keywords
» Artificial intelligence » Cnn » Lstm » Mae
