It's related to this line of thinking by Douglas Hoffstadter: https://en.wikipedia.org/wiki/Copycat_(software)
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On Thu, Oct 26, 2017 at 11:43 AM, Xavier Combelle <xavier.combe...@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.pitka...@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 <jas...@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 >>> >>> _______________________________________________ >>> Computer-go mailing list >>> Computer-go@computer-go.org >>> http://computer-go.org/mailman/listinfo/computer-go >>> >> >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > > > _______________________________________________ > Computer-go mailing > listComputer-go@computer-go.orghttp://computer-go.org/mailman/listinfo/computer-go > > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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