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

Jim O'Flaherty jim.oflaherty.jr at gmail.com
Thu Oct 26 05:40:56 PDT 2017


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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>
>
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