i was discussing the concepts of sente and kikashi with my opp in an online game review in which i was white (m..) and my opp was black (W..).

in the game black had played at L4.

i thought this was sente, but not kikashi. so white has a choice of defending or ignoring. we looked at how much territory white would get if he defends, and then i wanted to compare that with how much white could instead gain by playing elsewhere, such as extending D15 towards the black influence on the left so as to reduce it.

i wanted to draw a picture of influence to illustrate what i was saying. so i drew some circles on the board where i thought the boundary of black's influence was (see second picture).

the online client software doesn't have an influence map feature, so i then downloaded the sgf and fed it through pnprog's gomap and Leela 0.11.0 to see what they thought.

there are some differences, but many similarities, so i thought maybe a "concensus" view obtained by normalising and adding (or anding) the maps together would be interesting. this is what i got from a rough guess of how the sums would come out - my concensus map uses two influence values on points: strong = big blob, and weak= little blob; it is possible for a stone to be influenced by the other colour, although that isn't the case in this example - Leela thinks white L5 is surrounded by black influence, but gomap thinks it isn't. The circles are what i had drawn on the board during our review:

questions:
1. if you are a kyu, would such a map be useful to you for reviews or self-study?
2. if you are a dan, how accurate would you say the concensus map is? i had imagined that the black wall would extend influence as far as the circles (which i had drawn by hand at the time in our review), but both softwares had it stopping short of that - there is aji in the wall at F4 for example, so maybe it's not as thick as i had thought?

I have no idea what the precise answer is, but I can say this much:

- I'd rather be black in this game.
- I heard a proverb that when estimating the score, you should think of groups that are not alive as counting as negative 20 points on the board. It isn't just that you are not alive and may die, but your opponent can make points hassling your weak-ish groups. It is for this reason that I think B has a good position.
- It seems you got into trouble several moves before the snapshot you present. I think you would benefit from showing us the whole game.

I think influence maps are a red herring here and you should think about the fundamental concepts of connection and separation.

Before Monte Carlo Tree Search (MCTS) came on the scene and made a great advance in the skill level of go bots, go researchers had collectively spent 40 years trying to come up with a good influence functions and maps. Some of their attempts did incorporate the concepts of connection and separation. If they had been successful, MCTS bots would not have made such a marked advance.

I do think that an influence map produced by having a top bot play itself from the current position 1,000,000 times could be quite educational, however.

_________________
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— Winona Adkins

Visualize whirled peas.

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Bill, in case it wasn't clear, I didn't mean influence maps in general are a red herring (though as you say they haven't been useful in making a strong bot, plus isn't the left image pnprog's implementation of djhbrown's influence function?), but that on this particular board I think a kyu player would do better to think about the position and what will be good moves by sticking with basic ideas of connection rather than turning to an influence map for help.

On the more general side, a reservation I've always had is how is an influence map, most algorithms for which I've seen are basically some smooth proximity decay sort of function, going to cope with the non-smooth nature of go (stones have to be on the lines) and the way shifting one line can make a big difference to how much that player controls that area. For example what would an influence map say is going on in these corners?

Click Here To Show Diagram Code

[go]$$B White can easily live in corner
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ . . . . . X . . . |
$$ . . X . . . . . . |
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ ------------------+[/go]

Click Here To Show Diagram Code

[go]$$B White only has a ko in corner
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ . . . . . X . . . |
$$ . . . X . . . . . |
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ ------------------+[/go]

Click Here To Show Diagram Code

[go]$$B Probably black's corner
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ . . . . . , X . . |
$$ . . . X . . . . . |
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ ------------------+[/go]

Click Here To Show Diagram Code

[go]$$B Almost certainly black's corner
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ . . . . . , X . . |
$$ . . . . X . . . . |
$$ . . . . . . . . . |
$$ . . . . . . . . . |
$$ ------------------+[/go]

Or am I expecting such a map to do too much (more like an influence and likely-territory map). In order to reflect the invasion possibilities under stones on the 4th line perhaps one approach is the edge of the board can generate influence rays for the opposite colour (it's easy for the invader to make a base there) but that these can be overpowered by the influence rays from real stones but with a rapid decay after 2 lines travel.

Yes it is... up to some point: IIRC after several rounds of definition by him and implementation by me, djhbrown was still not 100% happy with the rule set he had devised for the map. And at that point I stopped working on this tool to fully focus on GRP.
Maybe a updated finale version of the map rules set is available on his blog: http://lcipm.blogspot.com/

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IMO, you hit the nail on the head.



I thought it might be fun to show the maps for my influence function. I dipped my toe into influence functions in the early 2000s. I found a function that produces correct territory estimates in certain well defined situations. To my surprise, when applied to the whole board, it produced a komi estimate of around 8. Surprisingly close. So I thought it might make a reasonable first approximation, despite its oversimplification and other flaws.

These days, I do think that it would be possible to train neural nets to produce good influence functions. Not that bots would use the functions in actual play, but they would be an aid to humans to help understand go positions.

My maps are quite simple, They indicate which points more than ⅓ belong to each player. (This is by fuzzy logic, not probability.)

Click Here To Show Diagram Code

[go]$$B Influence = 27.4
$$ . . . . . . . . . . |
$$ . . . . . . x . . . |
$$ . . . x x x x x . . |
$$ . . x x x x X x x . |
$$ . x x X x x x x . . |
$$ . . x x x x x . . . |
$$ . . . x . . . . . . |
$$ --------------------+[/go]

Click Here To Show Diagram Code

[go]$$B Influence = 25.9
$$ . . . . . . . . . . |
$$ . . . . x x x . . . |
$$ . . . x x x x x . . |
$$ . . x x x x X x x . |
$$ . . x x X x x x x . |
$$ . . . x x x x x . . |
$$ . . . . x x x . . . |
$$ --------------------+[/go]

Click Here To Show Diagram Code

[go]$$B Influence = 26.6
$$ . . . . . . . . . |
$$ . . . . . . x . . |
$$ . . . x x x x x . |
$$ . . x x x x X x x |
$$ . x x X x x x x . |
$$ . . x x x x x . . |
$$ . . . x . . . . . |
$$ ------------------+[/go]

Click Here To Show Diagram Code

[go]$$B Influence = 25.0
$$ . . . . . . . . . |
$$ . . . . x x x . . |
$$ . . . x x x x x . |
$$ . . x x x x X x x |
$$ . . x x X x x x x |
$$ . . . x x x x x . |
$$ . . . . x x x . . |
$$ ------------------+[/go]

It is obvious that this map underestimates the influence of the Black stones in the corner. (It also overestimates central influence.) For the last diagram I have a correction that assigns a surrounded block of fewer than 6 empty points to the surrounding player. That would correct the influence estimate to about 27 pts.



I don't think you are expecting too much. And I think that current neural networks could be trained to produce such maps. Right now I think that most go programmers are focused on improving play, however.

_________________
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.

I'm an independent go programmer experimenting with this.
If things proceed well in the next weeks/months, I *might* have a pro-level or superhuman-level bot that also will be able to output predictions of:

* The expected ownership of each point on the board (from -1 to 1).
* The expected final score outcome of the board (in points).

Actually, I have neural nets that do these things already, they're just not past amateur dan yet. So, still working on it...

This look great! keep us posted of any progress !

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Terrific!

_________________
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.

sgf attached as requested.


[admin] I'm assuming that you want it in the following form. -JB [/admin]

At move 19 you should l4 block, then if black cuts you can atari at m3 then n3 bump is a common tesuji, making miai of k2 capture and o4 to hurt black's corner and develop your shape to the centre.

P.S. I just checked with LZ, she agrees this is the best local answer, but slightly prefers to tenuki and enclose the top left corner at d17, allowing black to punch through at l4. This is because LZ sees any corner move higher than the 4th line as a mistake (punished by entering the corner) that urgently needs fixing by closing the corner. However, I'd disagree with this in a handicap game, you don't want to let black make the easy good strong shape at l4, and you don't mind black entering the top left as the stronger player tends to have the advantage with the more unusual high moves.

i tried out gomap on a hoshi-keima shimari, and it seemed to propagate clusters too far along the edge.

https://ssrn.com/abstract=3071677 contains this description:-



i'm wondering whether this rewrite might be more consistent with the associativity[1] of connectedness and the concept of recursive colour control described on page 9 of https://ssrn.com/abstract=2818149 :-


* the "newly-coloured" qualification avoids endless reiteration; in the first pass, stones are the first newly-coloured points, to get the ball rolling.

shadows are weaker than clusters, so maybe:


note: there are 2 kinds of shadows: black and white

1. http://mathworld.wolfram.com/Group.html

How about just enclosing the corner at move 15? (Although my preference would be to extend on the left side, to prevent Black from making an ideal extension from his wall in the bottom left.)

_________________
The Adkins Principle:
At some point, doesn't thinking have to go on?
— Winona Adkins

Visualize whirled peas.

Everything with love. Stay safe.

Of course LZ strongly prefers that as there is nothing urgent going on (like pushing into knight's move before), for #198 k3 loses 3% over d17 (but as 2 handi game we are already at 90% for b so 3% is relatively more than in an even game). Black punishes all the moves on the left side by invading at 3-3, except for c11 (the best, similar winrate to d17) which invades at 4-3 because c11 is close enough to be useful in enabling white to block at b12 when the usual c17 e17 joseki happens.
Perhaps more interesting is that LZ wants to 3-3 invade instead of 17, completing the gote wall joseki (which you shouldn't pick without a pre-extension on left). f2 sometimes being slack because it's easier to tenuki (than f4) is something I thought before AI, and a few weirdos on Tygem thought so too but I kinda dismissed it as "not proper go", but then AlphaGo agreed it's tenukiable in some variations in the teaching tool. (LZ would play f2 at g2 or b3)

While these are pretty pictures, I believe the whole concept is flawed and is misleading if you want to use it practically, since it is not even well defined what is "influence".

My main problem with these influence plots is that they miss the importance of cutting points and the ways to exploit them; a successful peep or cut (two basic ways to exploit a cutting point) can dramatically change the influence map in just a few moves.

_________________
Sorin - 361points.com

i like the look of this one, which it's programmer says she made in 2 days! i infer it uses a version of Zobrist's "oil and water" analogy. oh, wait - it takes Leela's heatmap as an input, so it might be a combination of Leela and Zobrist
https://github.com/featurecat/Influencie


it doesn't solve tsumego or avoid having to read out cuts, but it does indicate which stones have solid eyespace and which do not.

it also shows me the value of thickness; white has territory, but black has influence.

and white's upper middle stones look like they are in trouble and their only place to develop is the centre. black's last move looks like he is preparing to make even more thickness facing the centre, which will make white's job of living even harder.

given what happened in the upper right, i don't think the position is from a pro game, but i suppose it could be... can someone who can look up databases of pro games tell us?

The position is from a game Ota Yuzo vs. Yasuda Shusaku played in 1842.

Reading never was my strong point - i've only just now read viewtopic.php?f=18&t=16285, in which Lightvector makes a key observation:

Absolutely essential!

That seems to have been the cornerstone of gomap's "colour-control" - its algorithm doesn't quite achieve the stated intention of https://ssrn.com/abstract=2818149, but with a little modification, i think it could viewtopic.php?p=240279#p240279. It computes two qualitative strengths of connection (strong colour and weak shadow); strong doesn't necessarily imply immortality, but weak does imply being in danger - here there be dragones!! https://en.wikipedia.org/wiki/Here_be_dragons

i would love to see an influence map that would help me focus my reading to make it more efficient. i find reading so fatiguing that i can't imagine why anyone would want to be a Go professional - so much work for so little money! i suppose it's like pro tennis - the top of the pyramid are the rich 1%, but all the 99% think they can get to be in the 1%! - otherwise, why would they bother?

luckily for lesser mortal golf pros, there are many hackers like me willing to pay for being told the bleeding obvious, so they can make a living just from teaching and selling golf paraphernalia, but that's not generally true for Go - Guan Juo has done it successfully, but she is exceptional.

Leela's Territory markup doesn't imply connection, for example the picture of it in post #1 shows black "territory" (= influence?) spreading all around the white stone at L6 which has an open route to the centre.

https://www.reddit.com/r/cbaduk/comment ... influence/ includes discussion of LZ-generated "phantom" influence derived from later placement of stones in LZ's search tree; since influence emanates from existing stones, any eatnowp06-computed influence that is not connected* to a trail of influence ultimately touching an existing stone of the same colour can be removed. That will make the resulting map look more natural.

* at least doubly-connected to influence that is at least as great as it is, since influence can't suddenly increase out in open space, even if two clouds of same-coloured influence happen to meet. Doubly-connected so you don't end up with lots of thin trails one point wide. and where clouds of differently-coloured influence meet, there should be a sort of "no man's land" between them. Plus... jeez, so many caveats! - you would have to do a 2-pass computation, the first pass identifies definitely dead stones, and the second pass pretends they are not there so they don't get in the way.

the concensus influence map in post #1, reproduced here for convenience, shows me that white bottom left group has enough eyespace to live (even though black has several forcing moves which would be useful ko threats later on), black bottom right is strong but not definitely alive, and last but not least, white bottom middle doesn't have enough potential eyespace to make 2 eyes, but we can see an escape route in the direction of Q10-R6 which is not impeded by black influence.