https://www.youtube.com/watch?v=Om_CVAevU8o&list=PL4y5WtsvtduqNW0AKlSsOdea3Hl1X_v-S

Objective of this video series

To find a way to “see through the mind's eye” of an expert Go player (Hajin Lee 3P aka Haylee on YouTube) by drawing “pictures” of the mental images that enter her mind when she looks at a board position to decide what to do.

Purposes

1. To help people learning Go see more clearly

2. To identify generic patterns that a human expert uses to generate candidate moves and evaluate them, to create a set of generic heuristic rules that a computer could use to play “intelligent” Go.

Background

Contemporary computer go programs utilise “brute-force” search, involving evaluation of move sequences that a human expert would simply not even consider. This is partly because candidate move generation is non-trivial; in the absence of an intelligent knowledge-based candidate move generator, brute-force search is all a machine can do.

Go teachers use a combination of language and particular moves to explain general concepts, from which students can form their own mental images, but they still cannot see clearly what the teacher sees, because a lot of the knowledge an expert has is either tacit (subconscious) or too elaborate to explain in language alone.

So they typically resort to the Occam's Razor of asking students to choose between 2 or 3 moves (“would you play A, B or C?”).

But where do these choices come from? They come from inside the teacher's head, not from inside the student's head. To the teacher, the choices are obvious, they spring to mind.

But to the student (or computer) they emanate from within a cloud of darkness. Then the teacher shows what is likely to happen in each case, but again, each step in the follow-up sequence is also a move that is obvious to the teacher, but anything but obvious to the bewildered student.

It takes a long apprenticeship to become expert at anything, and Go is no exception. But maybe some pictures of mental images can shorten the journey of learning?