[Computer-go] Semeais

[Robert Jasiek]

Semeais can, in principle, be solved by expert systems, except that 
semeais with (basic) kos in general are a complex open research problem 
(my current estimate for 2-groups semeais is that there must be hundreds 
of different types!). Currently expert systems are a bit out of fashion 
but at least you might use a minimal knowledge approach:

1) Classify functionally equivalent moves.

2) Represent each class as one move / by one representative move.


Examples:

(A)

# # # # # # # # # #
. . . . . . . . . .
O O O O O O O O O O

You do not need 10! sequences here but only 1.

(B)

. . . . . . . . . .
# # # # # # # # # #
. . . . . . . . . .
O O O O O O O O O O
. . . . . . . . . .

Classify this into three move classes: outside of Black, outside of 
White, inside. Without expert knowledge, this is still too complex. 
Maybe you are lucky and a classified local simulation finds the correct 
answer. Expert knowledge surely does help a lot and reduces the 
sequences from 30! via 3^10 to 1.

(C)

. . . . . . . . . .
. # . . . . . . # .
# # # # # # # # # #
. . . . . . . . . .
O O O O O O O O O O
. . O . O . O . . .
. . . . . . . . . .

Paired approach defects and eye-threatening potential benefit from more 
expert knowledge, i.e., classify into more (sub)types.


Suggestion for MC:

1) Identify semeais by expert knowledge.

2) Divide the board into each semeai / non-semeai region.

3) Solve / represent each semeai by expert knowledge.

4) Do global MC with the change of special treatment for each semeai 
region. E.g., know the best black / white move in each semeai region and 
treat that region like just one intersection for the respective player 
to move (alternatively: as two intersections for either player).

For other problems like life and death, endgame or game's first move, a 
similar Local Expert Knowledge approach could be used.

There are complications of course:
- dynamic region update
- multiple threats on several regions
- ko


-- 
robert jasiek