This 'n' that
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EdLee
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Bill Spight
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Re: This 'n' that
Winona wanted to get married outdoors, and I was in charge of finding the place. I assumed that it would not be difficult to find room in a local park. And if we had just done it, that probably would have worked. But we wanted a reception afterwards, and that presented more of a problem.
Finally, with only a few weeks to go, I was getting a bit desperate. Then I found a web site by a couple who had a great deal of information about outdoor weddings in the East Bay Area. They mentioned the possibility of cemeteries. The Mountain View Cemetery turned out to be a very good choice. They host three to four weddings a year. In addition, their event coordinator took very good care of us.
Finally, with only a few weeks to go, I was getting a bit desperate. Then I found a web site by a couple who had a great deal of information about outdoor weddings in the East Bay Area. They mentioned the possibility of cemeteries. The Mountain View Cemetery turned out to be a very good choice. They host three to four weddings a year. In addition, their event coordinator took very good care of us.
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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EdLee
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dfan
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Re: This 'n' that
Bill Spight wrote:@ 2
is fairly obvious, making this in a way an easy problem. However, it is easy for the solver to miss
, which is White's best reply.
I don't know if this is the main line in the book, but it should be.
Indeed it is.
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Bill Spight
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Re: This 'n' that
Last edited by Bill Spight on Sat May 26, 2018 10:40 am, edited 1 time in total.
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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Bill Spight
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Re: This 'n' that
Winona and me a year ago
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- Winona Bill LR.jpg (162.58 KiB) Viewed 16834 times
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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EdLee
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Bill Spight
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Re: This 'n' that
Once more, dear friends. 
To quote myself, from my latest article in Myosu about getting the last play.
To quote myself, from my latest article in Myosu about getting the last play.
Moi wrote:What can we learn from the fight for the last play?
- * The fight for the last play typically occurs at temperature one, where gote and reverse sente sequences gain one point.
* To hold on to any gain at temperature one, there must be an actual or effective drop of one point in temperature, to zero.
* Since sente gains nothing, to gain one point a player has to take a reverse sente or gote.
* There are local positions where one player can get the last local play, no matter who plays first. If such a position is the last one remaining on the board, that player gets the last play on the whole board. The other player needs to play in such positions to prevent that from happening in order to get the last play.
* A reverse sente is such a position, so the player with sente should normally play it early; playing the sente does not gain any points, but it prevents the opponent from getting the reverse sente as the last play.
* There are local plays and positions that are ambiguous between sente and gote. In the fight for the last play they give an advantage to the player who can choose whether to play it as a sente or a gote.
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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Bill Spight
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Re: This 'n' that
Winona Adkins and kitty
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The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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Kirby
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Re: This 'n' that
Thank you, Bill, for the pictures. It's nice to put some names to faces.
be immersed
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Bill Spight
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Re: This 'n' that
Dear Friends and Gentle Hearts,
A little Valentine's present.
https://www.youtube.com/watch?v=WWLPwEF ... gs=pl%2Cwn
A little Valentine's present.
https://www.youtube.com/watch?v=WWLPwEF ... gs=pl%2Cwn
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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Bill Spight
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Re: This 'n' that
Many thanks to Daniel Hu for his thoughts about AlphaGo and strategy. viewtopic.php?f=10&t=16527
I think that we humans will come up new and useful ideas and concepts about go, based upon the play and opinions of bots, even if they cannot explain their plays and preferences. Hats off to Daniel for making steps in that direction.
I doubt if Elf (version 1, anyway) is as strong as AlphaGo, but the Elf team has given us a lot of material to study with its commentary on most of the GoGoD games. The commentary does not include handicap games, and its view of even games without komi is, perforce, flawed, as it was trained on 7.5 komi. I have also found a ladder mistake, BTW.
One remark in the paper by the Elf team has made a slight difference in my view of the frequent tenukis of Zero bots. It was that Elf learned go backwards, in a sense, because it learned to predict the winner of the game, and early in its training it was, OC, better at that near the end of the game than early in the game. (This does not prevent it from making wacko endgame plays, OC.) I filled in the blanks a little bit in that explanation, BTW.
The endgame, as we humans know, is the phase of the game in which, except for ko positions, the board is divided into independent or nearly independent regions. In each of these regions one play, or maybe a few plays are made, and then a player tenukis, because plays in that region have become worth less, or, as we say, the local temperature has dropped.
It is quite possible, then, that zero bots, who have not been trained on human games, view the go board is a number of quasi-independent regions, even in the opening. (I do so, myself, BTW.) Perhaps they do so even more than humans. Quien sabe? If so, that would explain their greater propensity to play tenuki than humans have.
Are humans wrong not to tenuki so much? Maybe, maybe not. To understand one reason why not we need to address the question of the margin of error of the bots' evaluations. (Nobody knows what the margin of error is, OC, because it has not been studied directly. IMHO, the margin of error of the winrate estimates of the AlphaGo teaching tool is at least 2%, and that of the winrate estimates of the Elf commentary is around 3%.) That is, if you are a human and Elf or Leela Zero says that you have made a 2% mistake, you may well have made the right play, and even if your move was an "inaccuracy", it may not cost any points with perfect play afterwards, and even if it does cost a point or two, it may not cost the game. That is especially so in the opening. OC, the bot as a player has to trust its evaluations, in accordance with its algorithms. So the bot may well tenuki (correctly) even when it does not actually need to do so, and a human in the same position would not be inclined to tenuki.
----
Here is a position when Takagawa, a player who understood the opening about as well as any human in the 20th century did, failed to tenuki, and Elf thinks that it was a 9% error. I'll go with Elf on this one. 9% is surely outside of its margin of error.
You can probably guess Takagawa's error, since he did not have many opportunities to make one. (It was 
.) But where should he have played , and why, according to Elf?
This was also the game where Elf missed the ladder.
Maeda played
as a ladder breaker. 
, OC, restored the ladder. Elf wants to play 
at 28. 
Edit: Let me add this.
One Elf variation has
at 30, restoring the ladder, but this one has White play 
instead of running out in the broken ladder. (This variations has only a small change in winrate.)
viewtopic.php?f=10&t=16527I think that we humans will come up new and useful ideas and concepts about go, based upon the play and opinions of bots, even if they cannot explain their plays and preferences. Hats off to Daniel for making steps in that direction.
I doubt if Elf (version 1, anyway) is as strong as AlphaGo, but the Elf team has given us a lot of material to study with its commentary on most of the GoGoD games. The commentary does not include handicap games, and its view of even games without komi is, perforce, flawed, as it was trained on 7.5 komi. I have also found a ladder mistake, BTW.
One remark in the paper by the Elf team has made a slight difference in my view of the frequent tenukis of Zero bots. It was that Elf learned go backwards, in a sense, because it learned to predict the winner of the game, and early in its training it was, OC, better at that near the end of the game than early in the game. (This does not prevent it from making wacko endgame plays, OC.
) I filled in the blanks a little bit in that explanation, BTW. The endgame, as we humans know, is the phase of the game in which, except for ko positions, the board is divided into independent or nearly independent regions. In each of these regions one play, or maybe a few plays are made, and then a player tenukis, because plays in that region have become worth less, or, as we say, the local temperature has dropped.
It is quite possible, then, that zero bots, who have not been trained on human games, view the go board is a number of quasi-independent regions, even in the opening. (I do so, myself, BTW.
) Perhaps they do so even more than humans. Quien sabe? If so, that would explain their greater propensity to play tenuki than humans have. Are humans wrong not to tenuki so much? Maybe, maybe not. To understand one reason why not we need to address the question of the margin of error of the bots' evaluations. (Nobody knows what the margin of error is, OC, because it has not been studied directly. IMHO, the margin of error of the winrate estimates of the AlphaGo teaching tool is at least 2%, and that of the winrate estimates of the Elf commentary is around 3%.) That is, if you are a human and Elf or Leela Zero says that you have made a 2% mistake, you may well have made the right play, and even if your move was an "inaccuracy", it may not cost any points with perfect play afterwards, and even if it does cost a point or two, it may not cost the game. That is especially so in the opening. OC, the bot as a player has to trust its evaluations, in accordance with its algorithms. So the bot may well tenuki (correctly) even when it does not actually need to do so, and a human in the same position would not be inclined to tenuki.
----
Here is a position when Takagawa, a player who understood the opening about as well as any human in the 20th century did, failed to tenuki, and Elf thinks that it was a 9% error. I'll go with Elf on this one. 9% is surely outside of its margin of error.
You can probably guess Takagawa's error, since he did not have many opportunities to make one.
(It was .) But where should he have played , and why, according to Elf?
. Later Elf anticipates the exchange, Black "a" - White "b". OC, Elf appreciates the relationship between the Black thickness in the top left and the Black thickness in the bottom left, but so do humans. But Takagawa did not realize the fact that the Black stones in the top left corner do not need an extension. (He even wrote about when stones need an extension, so I am pretty sure of what he thought. Maeda played
as a ladder breaker. , OC, restored the ladder. Elf wants to play at 28. Edit: Let me add this.
One Elf variation has
at 30, restoring the ladder, but this one has White play instead of running out in the broken ladder. (This variations has only a small change in winrate.)The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
At some point, doesn't thinking have to go on?
— Winona Adkins
Visualize whirled peas.
Everything with love. Stay safe.
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John Fairbairn
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Re: This 'n' that
Hi, Bill
I have printed out Daniel's paper to read on my next train journey, but as you have endorsed it I shall try to get to it sooner! I have a few remarks below on what you said, which obviously are made in ignorance of what Daniel may have said.
First, the trivial points. The move order shown for the Takagawa game (vs Maeda) is wrong, but of more substance is the fact that komi was only 4.5. Given that, which you correctly (as confirmed by the Elf programmer) see as a flaw, plus an apparent ladder error by Elf, I'm wondering how much we can rely on Elf being right here and Takagawa wrong.
Now the meatier points. You use the word thickness about Takagawa's play. It has been my impression that he himself tended to use that term far less than other players (and conversely used terms such a balance much more than other players). I put it to you that it's not real thickness (it's gaisei) and so the "rules" about local plays around thickness (such as extensions) actually tended to be either ignored by Takagawa or were subsumed in his views about balance. I think that has obvious implications for how we view AI moves in the whole nexus spanning thickness, influence, walls, thinness, etc. Even in the traditional human thinking on the topic, there is room for deeper consideration (e.g. the fact that thinness is not actually the antonym of thickness), but in any case the heuristics for playing around influence differ from playing around true thickness. I suggest bots may be doing that more reliably than humans.
Further (to take up your point about regions), one of the main tenets of New Fuseki theory was equilibrium and averaging based on symmetries (which, too, has a bearing on balance). Theorists then therefore made much of dividing the board diagonally rather than orthogonally, yet that idea seems to have been largely forgotten. Players today (even pros) seem focused still on viewing the board as four square quadrants, which I think is partly to do with obsession about josekis. But if you view the board as four equilateral triangles you get a very different view - perhaps one more like the one the bots are "seeing" but which may still be within the purview of humans?
I have printed out Daniel's paper to read on my next train journey, but as you have endorsed it I shall try to get to it sooner! I have a few remarks below on what you said, which obviously are made in ignorance of what Daniel may have said.
First, the trivial points. The move order shown for the Takagawa game (vs Maeda) is wrong, but of more substance is the fact that komi was only 4.5. Given that, which you correctly (as confirmed by the Elf programmer) see as a flaw, plus an apparent ladder error by Elf, I'm wondering how much we can rely on Elf being right here and Takagawa wrong.
Now the meatier points. You use the word thickness about Takagawa's play. It has been my impression that he himself tended to use that term far less than other players (and conversely used terms such a balance much more than other players). I put it to you that it's not real thickness (it's gaisei) and so the "rules" about local plays around thickness (such as extensions) actually tended to be either ignored by Takagawa or were subsumed in his views about balance. I think that has obvious implications for how we view AI moves in the whole nexus spanning thickness, influence, walls, thinness, etc. Even in the traditional human thinking on the topic, there is room for deeper consideration (e.g. the fact that thinness is not actually the antonym of thickness), but in any case the heuristics for playing around influence differ from playing around true thickness. I suggest bots may be doing that more reliably than humans.
Further (to take up your point about regions), one of the main tenets of New Fuseki theory was equilibrium and averaging based on symmetries (which, too, has a bearing on balance). Theorists then therefore made much of dividing the board diagonally rather than orthogonally, yet that idea seems to have been largely forgotten. Players today (even pros) seem focused still on viewing the board as four square quadrants, which I think is partly to do with obsession about josekis. But if you view the board as four equilateral triangles you get a very different view - perhaps one more like the one the bots are "seeing" but which may still be within the purview of humans?
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Aidoneus
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Re: This 'n' that
Hi, John
Would you care to expound a bit on how viewing the board split diagonally might lead to some different assessments? Perhaps you could point to some theoretical works translated into English?
Would you care to expound a bit on how viewing the board split diagonally might lead to some different assessments? Perhaps you could point to some theoretical works translated into English?