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

Jim O'Flaherty jim.oflaherty.jr at gmail.com
Thu Oct 26 15:54:41 PDT 2017

It's related to this line of thinking by Douglas Hoffstadter:


Jim O'Flaherty
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On Thu, Oct 26, 2017 at 11:43 AM, Xavier Combelle <xavier.combelle at gmail.com
> wrote:

> 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>
> 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>:
>>> 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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>>> Computer-go at computer-go.org
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