![]() ![]() For a dual task of predicting whether a human will make a large mistake on the next move, we develop a deep neural network that significantly outperforms competitive baselines. We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way. Applying existing chess engines to this data, including an open-source implementation of AlphaZero, we find that they do not predict human moves well. The hundreds of millions of games played online by players at every skill level form a rich source of data in which these decisions, and their exact context, are recorded in minute detail. The aggregate performance of a chess player unfolds as they make decisions over the course of a game. ![]() We pursue this goal in a model system with a long history in artificial intelligence: chess. A crucial step in bridging this gap between human and artificial intelligence is modeling the granular actions that constitute human behavior, rather than simply matching aggregate human performance. However, the ways in which AI systems approach problems are often different from the ways people do, and thus may be uninterpretable and hard to learn from. ![]() The code for training Maia can be found on our Github Repo.Īs artificial intelligence becomes increasingly intelligent-in some cases, achieving superhuman performance-there is growing potential for humans to learn from and collaborate with algorithms. If you want to see some more examples of Maia's predictions we have a tool here to see where the different models disagree. If you want to be the first to know, you can sign up for our email list here. We are going to be releasing beta versions of learning tools, teaching aids, and experiments based on Maia (analyses of your games, personalized puzzles, Turing tests, etc.). You can read a blog post about Maia from the Computational Social Science Lab or Microsoft Research. Search chess databases for positions, patterns and material balances.Read the full research paper on Maia, which was published in the 2020 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020).Explore chess openings and navigate easily through large chess databases.Promote and demote variations and even comments (so comments can change to moves and vice-versa – Enter variations easily (just make moves anywhere). View and play through the analysis provided by the engine. Setup an arbitrary position and get the engine to analyse that position.Play a game against a chess engine, with either color, from any position, with a time handicap if desired.Tarrasch is designed to to make it as easy as possible to perform some basic chess activities You'll love Tarrasch's slick database facilities, tabbed workspace, sharper/resizable/colourful graphics, improved large ![]() If you've only seen old versions of Tarrasch (2016 and earlier), Tarrasch Version 3 is a capable and full featured chess program. Tarrasch comes with a free database and chess engines, (including Stockfish and the demo versions of Houdini and Komodo), so you get everything you need to enjoy computerĬlick here to download Tarrasch for Windows. Tarrasch is an extremely easy to use free chess ![]()
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