Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

Let me add my congratulations to the chorus. Well done! I'm due for a sabbatical next year. I had been joking, "It sure would be good timing if someone cracked Go right before that started. Then I'd have plenty of time to pick a new research topic." It looks like AlphaGo has provided. On Wed,

Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

You must be kidding about Lee Sedol. Yes, he is not as dominating as before. (is it because he is weaker or because the other ones got better?) But he is still #3 in Korea having only dropped there this month, being #2 for most of the last year. (btw overtaken by Park Younghoon, who is not really

Re: [Computer-go] AlphaGo MCTS & Reinforcement Learning?

How about you read the paper first? The conversation would make much more sense if you actually spent some time trying to understand the details of what they did. :) <-- (mandatory smiley to indicate I am not upset or anything) On Sun, Jan 31, 2016 at 10:20 AM, Greg Schmidt

Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

t; propagates that information up the tree, that in and of itself would seem > > to constitute RL, so how does it make sense to have both? It seems > > redundant to me. Any thoughts on that? > > ___ > > Computer-go mailing list > > Computer-go@c

Re: [Computer-go] AlphaGo and the Standard Mistake in Research and Journalism

On 31.01.2016 17:19, John Tromp wrote: It will never be known since there's not enough space in the known universe to write it down. We're talking about a number with over 10^100 digits. How do you know that an implicit expression (of length smaller than 10^80) of the number does not exist?

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

Ingo and all, Why you care AlphaGo and DCNN so much? Surely DeepMind team did a big leap but the big problems, such as detecting double-ko and solving complex positions are left unchanged. Also it's well known that to attack these weakpoint of MCTS bots, the opponents have to be strong

[Computer-go] AlphaGo MCTS & Reinforcement Learning?

The articles I've read so far about AlphaGo mention both MCTS and RL/Q-Learning. Since MCTS (and certainly UCT) keeps statistics on wins and propagates that information up the tree, that in and of itself would seem to constitute RL, so how does it make sense to have both? It seems redundant

Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

> You must be kidding about Lee Sedol. > ... > So he was by far the biggest fish Google could ever catch for that > game, for Go insiders as well as for people outside the Go scene. Well said, Marc. In terms of name recognition and domination in the past decade, who else but Lee Sedol should be

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

Explaining why the move is good in human terms is useless goal. Good chess programs cannot do it nor it is meaningful. As the humans and computers have vastly different approach to selecting a move then by the definition have reasons for moves. As an example your second item 'long-term aji', For

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

On 31.01.2016 20:28, Peter Drake wrote: pick a new research topic. - explain by the program to human players why MC / DNN play is good in terms of human understanding of the game - incorporate the difficult parts, such as long-term aji - solve the game: prove the correct score, prove a weak

Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

Why would they water down their Lee Sedol game by announcing another game before their big game has even happened? No matter if that game would be before or after. Sounds like an awful PR strategy. 2016-02-01 2:51 GMT+01:00 uurtamo . : > It might even be interesting if it took

Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

Just in case that no one knows it. Ke Jie has publicly announced that he is willing to play against AlphaGo, even without any prize money. Since Ke Jie is absolutely the current No.1, it would be a good choice to have another match with Ke Jie, time permitting, no matter AlphaGo wins or loses

Re: [Computer-go] AlphaGo and the Standard Mistake in Research and Journalism

On 31.01.2016 19:57, John Tromp wrote: What is your best estimate of point where where chances are even? I do not know. what numbers the press could use that are not too arbitrary. - The number P of legal positions. - An empirical average number I of available intersections for the next

Re: [Computer-go] A proposition to improve neural network based on min max

i think similar approaches have been done. I can recall seeing it. Though in Backgammon they did train only by endresult and seemed to work fine. Originally anyway, now the have separate NN-for certain phases of the game 2016-01-30 18:07 GMT+02:00 Xavier Combelle : > I

Re: [Computer-go] Neural Nets to compare human playing strength

Hi Josef, thanks for the links to your interesting projects. In general, I think CNNs are sort of our new hammer. We should walk around (in our Go universe) and test the hammer on all possible questions... Perhaps we have reached iron age now, after (Crazy)Stone age... Ingo. Gesendet: 

[Computer-go] AlphaGo and the Standard Mistake in Research and Journalism

According to John Tromp et al at http://tromp.github.io/go/legal.html the number of legal 19x19 go positions is P19 = 2081681993819799846 9947863334486277028 6522453884530548425 6394568209274196127 3801537852564845169 8519643907259916015 6281285460898883144 2712971531931755773

Re: [Computer-go] AlphaGo and the Standard Mistake in Research and Journalism

dear Robert, > The number G19 of legal games under a given go ruleset is unknown. It will never be known since there's not enough space in the known universe to write it down. We're talking about a number with over 10^100 digits. > For positional > superko (prohibition of recreation of the same

Re: [Computer-go] AlphaGo MCTS & Reinforcement Learning?

On Sun, Jan 31, 2016 at 03:20:16PM +, Greg Schmidt wrote: > The articles I've read so far about AlphaGo mention both MCTS and > RL/Q-Learning. Since MCTS (and certainly UCT) keeps statistics on wins and > propagates that information up the tree, that in and of itself would seem to >