[Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

Xavier Combelle xavier.combelle at gmail.com
Thu Oct 26 09:43:16 PDT 2017

what are semantic genetic algorithm ?

to my knowledge genetic algorithm lead to poor result except as a
metaheuristic in optimisation problem

Le 26/10/2017 à 14:40, Jim O'Flaherty a écrit :
> When I get time to spend dozens of hours on computer go again, I plan
> to play in Robert's area with semantic genetic algorithms. I am an
> Architect Software Engineer. Robert's work will allow me better than
> starting entirely from random in much the same way AlphaGo
> bootstrapped from the 100K of professional games. AG0 then leveraged
> AlphaGo in knowing an architecture that was close enough. My intuition
> is my approach will be something similar in it's evolution.
> This is the way we're going to "automate" creating provided proofing
> of human cognition styled computer go players to assist humans in a
> gradient ascent learning cycle.
> So, Robert, I admire and am encouraged by your research for my own
> computer go projects in this area. Keep kicking butt in your unique
> way. We are in an interesting transition in this community. Stick it
> out. It will be worth it long term.
> On Oct 26, 2017 4:38 AM, "Petri Pitkanen" <petri.t.pitkanen at gmail.com
> <mailto:petri.t.pitkanen at gmail.com>> wrote:
>     Unfortunately there is no proof that you principles work better
>     than those form eighties. Nor there is any agreement that your
>     pronciples form any improvement over the old ones. Yes you are a 
>     far better player than me and shows that you are 
>     - way better at reading 
>     - have hugely better go understanding, principles if you like
>     What is missing that I doubt that you can verbalise your go
>     understanding to degree that by applying those principles  I could
>     become substantially better player. again bulleting
>     - My reading skills would not get any better hence making much of
>     value any learning moot. Obviously issue on me not on your principles
>     - your principles are more complex than you understand. Much of
>     you know is automated to degree that it is subconsciousness
>     information. Transferring that information if hard. Usually done
>     by re-playing master games looking at problems i.e. training the
>     darn neural net in the head
>     If you can build Go bot about  KGS 3/4dan strength I am more than
>     willing to admit you are right and would even consider buying
>     your  books.
>     Petri
>     2017-10-26 6:21 GMT+03:00 Robert Jasiek <jasiek at snafu.de
>     <mailto:jasiek at snafu.de>>:
>         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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