  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.
