On 25.10.2017 18:17, Xavier Combelle wrote:
exact go theory is full of hole.
WRT describing the whole game, yes, this is the current state. Solving
go in a mathematical sense is a project for centuries.
Actually, to my knowledge human can't apply only the exact go theory and
play a decent game.
Only for certain positions of a) late endgame, b) semeais, c) ko.
If human can't do that, how it will teach a computer to do it magically ?
IIRC, Martin Müller implemented CGT endgames a la Mathematical Go Endgames.
The reason why (b) had became unpopular is because there is no go theory
precise enough to implement it as an algorithm
There is quite some theory of the 95% principle kind which might be
implemented as approximation. E.g. "Usually, defend your weak important
group." can be approximated by approximating "group", "important" (its
loss is too large in a quick positional judgement), "weak" (can be
killed in two successive moves), "defend" (after the move, cannot be
killed in two successive moves), "usually" (always, unless there are
several such groups and some must be chosen, say, randomly; the
approximation being that the alternative strategy of large scale
exchange is discarded).
Besides, one must prioritise principles to solve conflicting principles
by a higher order principle.
IMO, such an expert system combined with tree reading and maybe MCTS to
emulate reading used when a principle depends on reading can, with an
effort of a few manyears of implementation, already achieve amateur mid
dan. Not high dan yet because high dans can choose advanced strategies,
such as global exchange, and there are no good enough principles for
that yet, which would also consider necessary side conditions related to
influence, aji etc. I need to work out such principles during the
following years. Currently, the state is that weaker principles have
identified the major topics (influence, aji etc.) to be considered in
fights but they must be refined to create 95%+ principles.
***
In the 80s and 90s, expert systems failed to do better than ca. 5 kyu
because principles were only marginally better than 50%. Today, (my)
average principles discard the weaker, 50% principles and are ca. 75%.
Tomorrow, the 75% principles can be discarded for an average of 95%
principles. Expert systems get their chance again! Their major
disadvantage remains: great manpower is required for implementation. The
advantage is semantical understanding.
--
robert jasiek
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