The AI model has surpassed human players in the game of Go, and it is widely
believed that the AI model has encoded new knowledge about the Go game beyond
human players. In this way, explaining the knowledge encoded by the AI model
and using it to teach human players represent a promising-yet-challenging issue
in explainable AI. To this end, mathematical supports are required to ensure
that human players can learn accurate and verifiable knowledge, rather than
specious intuitive analysis. Thus, in this paper, we extract interaction
primitives between stones encoded by the value network for the Go game, so as
to enable people to learn from the value network. Experiments show the
effectiveness of our method.

AI 模型在围棋游戏中超越了人类选手，解释 AI 模型所编码的围棋知识并将其用于教导人类选手代表着一个具有前景但具有挑战性的问题。为此，本文提取围棋游戏价值网络所编码的棋子之间的交互原语，以使人们能够从价值网络中学习。实验表明了我们方法的有效性。

解析神经网络对弈围棋及人类学习

Explaining How a Neural Network Play the Go Game and Let People Learn

How will superhuman artificial intelligence (AI) affect human decision
making? And what will be the mechanisms behind this effect? We address these
questions in a domain where AI already exceeds human performance, analyzing
more than 5.8 million move decisions made by professional Go players over the
past 71 years (1950-2021). To address the first question, we use a superhuman
AI program to estimate the quality of human decisions across time, generating
58 billion counterfactual game patterns and comparing the win rates of actual
human decisions with those of counterfactual AI decisions. We find that humans
began to make significantly better decisions following the advent of superhuman
AI. We then examine human players' strategies across time and find that novel
decisions (i.e., previously unobserved moves) occurred more frequently and
became associated with higher decision quality after the advent of superhuman
AI. Our findings suggest that the development of superhuman AI programs may
have prompted human players to break away from traditional strategies and
induced them to explore novel moves, which in turn may have improved their
decision-making.

通过分析职业围棋选手过去 71 年中超过 580 万次决策，使用超级人工智能程序评估人类决策质量并比较人类决策和人工智能决策的胜率，结果表明，超级人工智能的出现促使人类玩家从传统策略中脱颖而出，导致他们去探索一些新的策略，从而提高了决策能力。

超级人工智能可以通过增加新奇性来改善人类决策能力

Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty

The game of Go is more challenging than other board games, due to the
difficulty of constructing a position or move evaluation function. In this
paper we investigate whether deep convolutional networks can be used to
directly represent and learn this knowledge. We train a large 12-layer
convolutional neural network by supervised learning from a database of human
professional games. The network correctly predicts the expert move in 55% of
positions, equalling the accuracy of a 6 dan human player. When the trained
convolutional network was used directly to play games of Go, without any
search, it beat the traditional search program GnuGo in 97% of games, and
matched the performance of a state-of-the-art Monte-Carlo tree search that
simulates a million positions per move.

本文旨在通过深度卷积网络对围棋高手的数据进行监督学习，以期望训练神经网络自行评估围棋中的局面，并在二人对弈时直接利用该神经网络，结果显示其胜率达到了 97%。