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Aligning Superhuman AI with Human Behavior: Chess as a Model System
attributed to: Reid McIlroy-Young, Siddhartha Sen, Jon Kleinberg, Ashton Anderson
As artificial intelligence becomes increasingly intelligent---in some cases,
achieving superhuman performance---there is growing potential for humans to
learn from and collaborate with algorithms. 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. 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.
We pursue this goal in a model system with a long history in artificial
intelligence: chess. The aggregate performance of a chess player unfolds as
they make decisions over the course of a game. 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. Applying existing chess engines to this data, including an open-source
implementation of AlphaZero, we find that they do not predict human moves well.
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. 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. Taken together, our results suggest that there is substantial
promise in designing artificial intelligence systems with human collaboration
in mind by first accurately modeling granular human decision-making.
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Vulnerabilities & Strengths