Summary of Human-aligned Chess with a Bit Of Search, by Yiming Zhang et al.
Human-aligned Chess with a Bit of Search
by Yiming Zhang, Athul Paul Jacob, Vivian Lai, Daniel Fried, Daphne Ippolito
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces Allie, a chess-playing AI designed to model human-like behaviors in chess. Allie is trained on log sequences of real chess games and outperforms existing state-of-the-art models in human chess move prediction. The model learns to assign reward at each game state, which can be used for time-adaptive Monte-Carlo tree search (MCTS) procedures. This adaptive search enables skill calibration and leads to a remarkable 49 Elo gap in a large-scale online evaluation against players with ratings from 1000 to 2600 Elo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chess has been a testbed for AI’s quest to match human intelligence, but current systems can’t model human-like behaviors beyond piece movement. The paper introduces Allie, an AI that bridges this gap by learning from log sequences of real chess games. Allie outperforms existing models in move prediction and exhibits human-like behavior, including “pondering” at critical positions. |
