We're happy to announce a research project that has been in the works for almost two years! Please meet Maia, a human-like neural network chess engine. Maia is a Leela-style framework that learns from human play instead of self-play, with the goal of making human-like moves instead of optimal moves. Maia predicts the exact moves humans play in real online games over 50% of the time. We intend Maia to power data-driven learning tools and teaching aids, as well as be a fun sparring partner to play against.
We trained 9 different versions on 12M Lichess games each, one for each rating level between 1100 and 1900. Each version captures human style at its targeted level, meaning that Maia 1500's play is most similar to 1500-rated players, etc. You can play different versions of Maia yourself on Lichess: Maia 1100 (https://lichess.org/@/maia1), Maia 1500 (https://lichess.org/@/maia5) and Maia 190 (https://lichess.org/@/maia9)
This is an ongoing research project using chess as a model system for understanding how to design machine learning models for better human-AI interaction. For more information about the project, check out http://maiachess.com. We published a research paper (https://arxiv.org/abs/2006.01855) and blog post (http://csslab.cs.toronto.edu/blog/2020/08/24/maia_chess_kdd/) on Maia, and the Microsoft Research blog (https://www.microsoft.com/en-us/research/blog/the-human-side...) covered the project here. All of our code is available on our [GitHub repo](https://github.com/CSSLab/maia-chess). We are super grateful to Lichess.org for making this project possible with their open data policy.
In current work, we are developing Maia models that are personalized to individual players. It turns out that personalized Maia can predict a particular player's moves up to 75% of the time. You can read a preprint about this work https://arxiv.org/abs/2008.10086.
We'd love to hear your feedback! You can contact us at maiachess@cs.toronto.edu or on our new Twitter account @maiachess.
