Life In 19x19

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[go]$$B Avalanche $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . b . . . | $$ | . . . . . . . . . . . . . . 3 O c . . | $$ | . . . O . . . . . , . . . . . 1 2 . . | $$ | . . . . . . . . . . . . . . . X O . . | $$ | . . . . . . . . . . . . . . . a 4 . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . X . . . . . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ ---------------------------------------[/go]

The avalanche is not so popular anymore, although it's only a fraction of a point (0.3) worse than the hane.

Obviously is sente (19.2 > 7.9). So is (15.3 > 12.1).
is also sente but what surprised me is that is "even more" sente than (19.8 > 7.5). Intuitively allowing Black to cut through the keima with a solidly connected triangle is worse than allowing atari to one stone.
cools down the position but remains ambiguous: not only is the local temperature (12.9) only slightly bigger than the ambient (12.7), also KataGo wants to play elsewhere now, against the temperature argument.

Indeed, an analysis that includes passing doesn't necessarily agree with what's the best move. It might mean that my version of temperature used here is not well defined.

There are other moves available for , most notably A which leads to the small avalanche. This variation keeps the area "hot" for a while and can even be considered to turn into a "whole board joseki"

It remains interesting that an increase of local temperature seems to always induce a decrease of the ambient temperature. In the words of the definition used: a high value of a local move decreases the value of the biggest move elsewhere. This is somewhat intuitive but not trivial. A priori one might expect that ambient temperature remains relatively stable and only local temperature goes up or down considerably. But that is not the case. Allowing an early atari in a corner makes the position in that corner lopsided, so that the relative value of the other corners decreases.

Knotwilg wrote:I applied the earlier reasoning on another joseki. This time I put 4-4 stones in every other corner, reducing the ambient temperature (I think).

If you really want to reduce the ambient temperature, try 3-3 points? Although I'm not sure if minimising the ambient temperature is actually what you want...

Knotwilg wrote:There's SL's definition by Bill, closely related to CGT: https://senseis.xmp.net/?Temperature
I have added my own understanding: https://senseis.xmp.net/?Dieter%2FTemperature

I just have read your new version of temperature on sensei.

I completly agree with your defintion:

"The temperature (T) of a board position is the difference between playing a move and passing in that position."

But for the late endgame I cannot agree wtih you:

Late endgame
In the (late) endgame, we can treat local positions as isolated, since their outcome won't affect other positions. In that case, we consider the local temperature to be the temperature as above, assuming that the local position is alone on the board.

Let me try to explain. You cannot confused the temperature of a board position with the temperature of local position with a more a less "ideal" environment. These two situations are very different.
It will be simpler to understand with an exemple.
Let's take a game made of pure gotes area without follow-ups with the miai values 14, 13, 12, ... 2, 1 or swing values 28, 26, 24, ... 4, 2

What is the temperature of the board

S(P,B) = 14 - 13 + 12 ... + 2 - 1 = +7
S(P,W) = -14 + 13 - 12 ...- 2 + 1 = -7
T(P) = |S(P,B) - S(P,W)| = +7 + 7 = +14

Now what is temperature of the biggest local area I call G (miai 14 or swing value 28).
If you consider this area G is alone on the board then
S(G,B) = 14
S(G,W) = -14
T(G) = |S(G,B) - S(G,W)| = +14 + 14 = +28

As you see you cannot handled in the same way the all board and a local area.

kvasir wrote:

Gérard TAILLE wrote:I note also a secondary point in your post. In the fuseki the temperature between two joseki is more probably equal to 12 rather than equal to 14. Maybe someone can confirm this point with more playouts.

I did an analysis of how KataGo evaluates handicap stones to komi. It is something I might share. I found that if h is the handicap then 13.5 * (h - 0.5) - 0.25 is a function for the komi that KataGo evaluates as fair after substantial playouts. This function was designed to give whole or half komi A robust estimator found 13.33 and if we are only interested in small handicaps then 13.2 was the average for the first 3 handicap stones.

I actually wanted to make more analysis on it before mentioning it here. One question I have is if this komi actually give equal winning chances in games. I know it is not that far off for 9 stones, black wins some and white wins some.

Basically, I can confirm 13.2 in the early game with open corners.

Btw the range was [12, 15.5] for handicap stones. I have seen it down to about 10 in actual games during the opening, maybe even less.

==Edit
I meant to write 13.5 * (h - 0.5) - 0.25 when I wrote 13.5 * (h - 0.5) + 0.25, so I corrected this.

You said that with open corner and according to katago, the temperature of the board is about 13.2. That means that with a komi = 6.5 black as a very small advantage.
With not a lot of playouts katago seems to give advantage to white in the initial position with komi = 6.5.
I would be interesting to know how many playouts is needed to find a black advantage in these conditions?
Robert said that millions of playouts are needed to begin to trust katago's results. is it the case to find the best komi according to katago?

Gérard TAILLE wrote: Robert said that millions of playouts are needed to begin to trust katago's results.

I have said that 100k per top candidate is about my lower bound of trust. Depending on circumstances, (many) millions can be necessary.

Anecdote: Just today, I have had 500k for each of the top three moves. A or B seemed correct while C seemed to be a mistake. These assessments were stable since 100k. Nevertheless, I smelled the fish and continued search. Suddenly, A and B became mistakes while C became correct. Stable afterwards. Ugh!

RobertJasiek wrote:Anecdote: Just today, I have had 500k for each of the top three moves. A or B seemed correct while C seemed to be a mistake. These assessments were stable since 100k. Nevertheless, I smelled the fish and continued search. Suddenly, A and B became mistakes while C became correct. Stable afterwards. Ugh!

Interesting! Would you be willing to share the position, to see if others can replicate these results or offer any other insights? What features caused it to seem fishy?

There was a lot of discussion a few years ago about interrogating the AI: not just letting it run for a long time on a single position, but exploring variations, alternative candidate moves, followups, possible refutations and so on -- looking for supplementary information to better "understand" its choice.

xela wrote:Would you be willing to share the position

Not at this moment, as it would reveal too closely what I am working on. You can ask me again in a few months.

What features caused it to seem fishy?

The position is a joseki branch at a moment when each of the three interesting candidates leads to complicated follow-ups with very different developments after either moving out immediately or first going for the local territory and eyespaces. Therefore, I suspected that not only I but also AI might have difficulties to assess the candidates with an only intermediate amount of thought.

The early values of the alleged mistakes were just beyond the threshold which I have chosen to identify mistakes. They were not far beyond the threshold. Accordingly, still I was sceptical. My preferred threshold is 2.0 for the sum of the differences of winning percentages and empirical scores compared to the current top candidate.

There was a lot of discussion a few years ago about interrogating the AI: not just letting it run for a long time on a single position, but exploring variations, alternative candidate moves, followups, possible refutations and so on -- looking for supplementary information to better "understand" its choice.

Besides doing all of that, what have been the suggestions?

RobertJasiek wrote:

Gérard TAILLE wrote: Robert said that millions of playouts are needed to begin to trust katago's results.

I have said that 100k per top candidate is about my lower bound of trust. Depending on circumstances, (many) millions can be necessary.

Anecdote: Just today, I have had 500k for each of the top three moves. A or B seemed correct while C seemed to be a mistake. These assessments were stable since 100k. Nevertheless, I smelled the fish and continued search. Suddenly, A and B became mistakes while C became correct. Stable afterwards. Ugh!

Yes Robert it is not surprising. If the position needs a very deep analysis with typically a difficult semeai then katago can easily missed the best move for a long time.
I showed already some endgames that need a deep analysis. In such situation katago also is unable to find the best endgame sequence.