On Mar 13, 2016, at 6:00 AM, [email protected] wrote:
>
>> So, what would be Lee's best effort to exploit this? Complicating
>> and playing hopefully-unexpected-tesuji moves?
Judging from this game, setting up multiple interrelated tactical fights, such
that no subset of them works, but all together they work to capture or kill
something.
For tactical fights, I would expect the value network to be relatively weaker
than for quiet territorial positions.
So it comes down to solving the problem by search.
Aja and me wrote a paper a few years back that showed that even on a 9x9 board,
having two safe but not entirely safe-in-playouts groups on the board confuses
most Go programs and can push the “bad news” over the search horizon. Now
imagine having 3, 4, 5 or more simultaneous tactics. The combinatorics of
searching through all of those by brute force are enormous. But humans know
exactly what they are looking for.
Martin
Reference:
http://webdocs.cs.ualberta.ca/~mmueller/publications.html#2013
S.-C. Huang and M. Müller. Investigating the Limits of Monte Carlo Tree Search
Methods in Computer Go
<http://webdocs.cs.ualberta.ca/~mmueller/ps/2013/2013-CG-MCTS-Go-Limits.pdf>.
Computers and Games 2013, p. 39-48.
Erratum
<http://webdocs.cs.ualberta.ca/~mmueller/ps/2013/2013-CG-MCTS-Go-Limits-erratum.txt>
for this paper - in test case 2 Black wins.
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