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" <[email protected] > <mailto:[email protected]>> 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 <[email protected] > <mailto:[email protected]>>: > > 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 > [email protected] <mailto:[email protected]> > http://computer-go.org/mailman/listinfo/computer-go > <http://computer-go.org/mailman/listinfo/computer-go> > > > > _______________________________________________ > Computer-go mailing list > [email protected] <mailto:[email protected]> > http://computer-go.org/mailman/listinfo/computer-go > <http://computer-go.org/mailman/listinfo/computer-go> > > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://computer-go.org/mailman/listinfo/computer-go
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