Life In 19x19

Posted: **Tue Jun 02, 2020 4:33 am**

Uberdude wrote:Back on the PGETC case (2 years ago! shame we don't have better automated systems like we talked about way back then), the matching metric used as evidence someone was cheating, subsequently overturned on appeal, was "game move was in top 3 choices of Leela 0.11". Here's some stats for that matching metric, and top 1 choice, I collected. Note the pros were lower than mid and high dan amateurs, probably because they are stronger than old Leela (this was before superhuman LeelaZero, KataGo etc).

Code: Select all

+-----------------+------+----------------+------+---------+---------+---------+---------+
|      Black      | Rank |     White      | Rank | B top 3 | W top 3 | B top 1 | W top 1 |
+-----------------+------+----------------+------+---------+---------+---------+---------+
| [Carlo Metta]   |  4d  | Reem Ben David |  4d  |    * 98 |      80 |    * 72 |      54 |   http://pandanet-igs.com/system/sgfs/6374/original/WWIWTFDSGS.sgf
| Andrey Kulkov   |  6d  | [Carlo Metta]  |  4d  |      80 |    * 86 |      68 |    * 62 |   http://pandanet-igs.com/system/sgfs/6314/original/AMTRMFSDAB.sgf
| Dragos Bajenaru |  6d  | [Carlo Metta]  |  4d  |      74 |    * 78 |      50 |    * 60 |   http://pandanet-igs.com/system/sgfs/6354/original/JRZPCWSANY.sgf
| [Andrew Simons] |  4d  | Jostein Flood  |  3d  |      80 |      88 |      54 |      62 |   http://pandanet-igs.com/system/sgfs/6612/original/XSJUGZZTOX.sgf
| Geert Groenen   |  5d  | [Daniel Hu]    |  4d  |      74 |      66 |      40 |      46 |   http://britgo.org/files/pandanet2016/mathmo-GGroenen-2017-01-10.sgf
| [Ilya Shikshin] |  1p  | Artem Kachan.  |  1p  |      56 |      76 |      38 |      60 |   http://pandanet-igs.com/system/sgfs/6384/original/RYSGTEGMXT.sgf
| [Andrew Simons] |  4d  | Victor Chow    |  7d  |      84 |      76 |      44 |      44 |   http://britgo.org/files/pandanet2014/RoseDuke-Egmump-2015-01-13.sgf
| Cornel Burzo    |  6d  | [A. Dinerstein]|  3p  |      74 |      66 |      40 |      48 |   http://pandanet-igs.com/system/sgfs/6349/original/SCNSFSJXTI.sgf
| Jonas Welticke  |  6d  | [Daniel Hu]    |  4d  |      54 |      64 |      34 |      42 |   http://britgo.org/files/pandanet2017/mathmo-iryumika-2017-12-12.sgf
| [Park Junghwan] |  9p  | Lee Sedol      |  9p  |      74 |      64 |      64 |      38 |   http://www.go4go.net/go/games/sgfview/68053
| Lothar Spiegel  |  5d  | [Daniel Hu]    |  4d  |      66 |      58 |      48 |      42 |   http://britgo.org/files/pandanet2016/mathmo-Mekanik-2017-04-25.sgf
| Gilles v.Eeden  |  6d  | [Viktor Lin]   |  6d  |      82 |      70 |      56 |      46 |   http://pandanet-igs.com/system/sgfs/6616/original/FMKVQBHBBV.sgf
+-----------------+------+----------------+------+---------+---------+---------+---------+

As a chart at https://www.lifein19x19.com/viewtopic.p ... 60#p229460

From that table, I would say that the data is inconclusive, because there is not a lot of correlation between the player grade and the match percentage.
For example, Carlo Metta 4d, Reem Ben David 4d, Jostein Flood 3d and Andrew Simons 4d all have a higher match than than the average 6d+ players.
I suppose that might be explained by these players training a lot with Leela, perhaps specifically to prepare for this tournament.

The 98% in the first row is very high, but his opponent also had a high top 3 match in that game. So it could still be explained by this particular game happening to be similar to the kind of game that Leela plays, perhaps because both players trained a lot with Leela.

So my feeling is that these number are insufficient to draw a solid conclusion. I think the game content should at least be scrutinized in more detail by some human experts (do they exist?).

Posted: **Tue Jun 02, 2020 4:44 am**

In a combination of naive thought, and vague recollection of what Ken Regan was doing, don't you need something like a playing strength histogram for all ply.

Posted: **Tue Jun 02, 2020 5:30 am**

Javaness2 wrote:Don't you need to look at the percentage of matches, rather than the number of matches?
In a combination of naive thought, and vague recollection of what Ken Regan was doing, don't you need something like a playing strength histogram for all ply.

The metric in my table was % of matches between some moves numbers I can't recall, maybe 30 and 130. I don't think it's a good way of detecting cheaters. I'd have thought, now we have KataGo able to give measurements of mistakes in points rather than winrate (which is pretty useless if one player leading "big"), that the kind of point loss histogram like moha made in https://www.lifein19x19.com/viewtopic.p ... 89#p255489 is the way to go if you want to compare distributions. Even better is some way to focus how well they played the difficult rather than easy moves (e.g. moves matching LZ that don't match Leela classic, which can be taken as an approximation of a strong human amateur pre AI), but even that doesn't cope with how humans play like AI now (but most obviously in the opening, so perhaps only look at moves e.g. 40+).

Posted: **Tue Jun 02, 2020 6:08 am**

On telling bot play from human play

OC, on average the bot plays are better. And perhaps the question should be how good a player's plays are. Ken Regan developed a way of giving chess plays ELO ratings.

Following are 12 examples where one play was made by a human, but a bot (Elf in this case, as the examples come from the Elf commentaries) not only gave a different play as its top choice, but gave the human play very few rollouts. To control for the quality of the play, their winrate estimates are within 2.0%. Since winrate estimates of plays with few rollouts are unreliable, Elf took the estimate from its winrate estimate of its choice of reply to the human play.

Example 1

Click Here To Show Diagram Code: [go]$$Wc Move 2 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . a . . . | $$ | . . b . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ ---------------------------------------[/go]

Of a or b, which did Elf choose, which did the human choose? The winrate difference is 1.2%. Elf's choice got 23k rollouts, the human play got 2. (Not 2k, 2.

)

Posted: **Tue Jun 02, 2020 6:20 am**

Example 2

Maybe not so easy.

Click Here To Show Diagram Code: [go]$$Wc Move 6 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . X , a . . . . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . O . . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . O . . . . . . . . . . . . X b . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ ---------------------------------------[/go]

Who played the high approach? Who played the attachment?

Winrate difference, 1.6%. Elf' rollouts: 28k, human's rollouts: 1.

Posted: **Tue Jun 02, 2020 6:33 am**

Example 3.

Click Here To Show Diagram Code: [go]$$Bc Move 23 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . X X O . a . . O . . . . . . . . | $$ | . . X , O . O . . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . X . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . O . . . | $$ | . . . , . b . . . , . . . X O , . . . | $$ | . . O . . . . . . . . . . X O X O . . | $$ | . . . . . . . . . . . . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Who played the invasion, who played the approach?

Winrate difference: 1.9%. Elf's play got 21k rollouts, the human play got 183.

Elf played the invasion.

I might have gotten this one, as the approach is close to Black's strength in the bottom right.

Elf's main variation:

Click Here To Show Diagram Code: [go]$$Bcm23 Mainline $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . 2 . . . . . . . . . . . . | $$ | . . . X X O . 1 . . O . . 3 . . . . . | $$ | . . X , O . O a . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . X . . . . . . . . . . . . . 4 . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . 8 . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . 6 . . . . . . . . . . . O . . . | $$ | . . . , . . 7 . . , . . . X O , . . . | $$ | . . O . 5 . . . . . . . . X O X O . . | $$ | . . . . . . . . . . . . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Instead of playing

first to prepare the invasion, Elf starts with the invasion. We might consider the invasion a probe, as White might choose a different reply, such as a. Elf prefers the one space low approach to the bottom left corner.

Posted: **Tue Jun 02, 2020 6:48 am**

What's the point of flooding this thread with endless diagrams? This is not what it's about.

Posted: **Tue Jun 02, 2020 6:52 am**

Example 4.

Click Here To Show Diagram Code: [go]$$Wc Move 28 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . a O . . . . | $$ | . . . X X O . . . . O . X . X . . . . | $$ | . . X , O . O . . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . X . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . b . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . O . . . . . . . . . . . . O . . . | $$ | . . . , . X . . . , . . . X O , . . . | $$ | . . O . . . . . . . . . . X O X O . . | $$ | . . . . . . . . . . . . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Who crawled in the corner, who extended on the side?

Winrate difference: 0.6%. Elf's play got 53k rollouts, the human's play got 0.

Elf played the crawl.

Well the number of rollouts was perhaps a big clue. How many rollouts does it take to explore an extension on the side?

Elf's main variation:

Click Here To Show Diagram Code: [go]$$Wcm28 Mainline $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . 3 1 O . . . . | $$ | . . . X X O . . . . O . X . X . 2 . . | $$ | . . X , O . O . . , . . . . . X . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . X . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . 9 . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . 7 . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . 6 . . | $$ | . . . . . . . . . . . . . . 8 . . . . | $$ | . . O . . . . . . . . . . . . O . . . | $$ | . . . 4 . X . . . , . . . X O , . . . | $$ | . . O 5 . . . . . . . . . X O X O . . | $$ | . . . . . . . . . . . . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

After

,

took the 3-3 and then

connected underneath. Black's approach with

is interesting, aiming at

, a key point for the joseki in the bottom right.

Posted: **Tue Jun 02, 2020 6:54 am**

Adin wrote:What's the point of flooding this thread with endless diagrams? This is not what it's about.

The point of the diagrams is telling the difference between human plays and bot plays. Which is the point of the human matching the bot's plays or not, isn't it?

If matching is relevant, so is not matching.

Edit: Besides, by choosing human plays that got few rollouts, if any, it is unlikely that they would match any of the bot's top three choices.

Posted: **Tue Jun 02, 2020 6:57 am**

Whatever method the bot detector is using, I think the bot detector should be tested thoroughly (similar to clinical testing) to determine the amount of false positives and false negatives it gives in different scenarios (multiple players who train a lot with AI; no cheating, cheating only for some important moves, cheating for many moves per game, activily trying to outsmart the detector while cheating, actively trying to play very AI-like without cheating, ...).

From such testing we may determine if we can get sufficiently close to Blackstone's ratio.

Posted: **Tue Jun 02, 2020 7:14 am**

The point of the diagrams is telling the difference between human plays and bot plays.

The ones reading this forum are humans, not bots or bot detectors. And it does not matter what humans think looking at those diagrams because very subtle value differences between reasonable plays is certainly NOT what a human investigator is looking for. What a human investigator is looking for is mostly plays that are highly unusual for the rank of the player (or for any human players). And other stuff which I will not detail here in case cheaters are reading. But certainly not if the first move is 4X4 or 3X3.

Posted: **Tue Jun 02, 2020 7:19 am**

Example 5

Click Here To Show Diagram Code: [go]$$Wc Move 66 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . X O O . . . | $$ | . . . X X O . . . . O . X X X O O O . | $$ | . . X , O . O . . , . . . . O X X O . | $$ | . . . . . . . . . . . . . . O . . X . | $$ | . . X . . . . . . . . . . X . X X . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . O . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . O . . | $$ | . . . . . . . . . . . . . . X . . . . | $$ | . . . . . X . . . X . . . . X O . . . | $$ | . . O . . . . a X O X X . . . O . . . | $$ | . . . O . X b X O O O . . X O , . . . | $$ | . . O X X X O . O . . . . X O X O . . | $$ | . . O . . X O . . . . . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Who played the atari, who pushed in between?

Winrate difference: 0.4%. Elf's play got 111k rollouts, the human's play got 475.

Elf pushed in between.

Elf's main variation.

Click Here To Show Diagram Code: [go]$$Wcm66 Mainline $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . X O O . . . | $$ | . . . X X O . . . . O . X X X O O O . | $$ | . . X , O . O . . , . . . . O X X O . | $$ | . . . . . . . . . . . . . . O . . X . | $$ | . . X . . . . . . . . . . X . X X . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . O . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . O . . | $$ | . . . . . . . . . . . . . . X . . . . | $$ | . . . . . X . . 4 X . . . . X O . . . | $$ | . . O . . . 2 . X O X X . . . O . . . | $$ | . . . O . X 1 X O O O . . X O , . . . | $$ | . . O X X X O 3 O . . . . X O X O . . | $$ | . . O . . X O . . . 5 . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Posted: **Tue Jun 02, 2020 7:27 am**

Adin wrote:
The point of the diagrams is telling the difference between human plays and bot plays.
The ones reading this forum are humans, not bots or bot detectors. And it does not matter what humans think looking at those diagrams because very subtle value differences between reasonable plays is certainly NOT what a human investigator is looking for. What a human investigator is looking for is mostly plays that are highly unusual for the rank of the player (or for any human players). And other stuff which I will not detail here in case cheaters are reading. But certainly not if the first move is 4X4 or 3X3.

(Emphasis mine.)

If you are looking for unusual plays for a human, then you should be able to see one or two in these comparisons, shouldn't you?

As for including the example of move 2, as a scientist I decided upon my criteria first, without considering which moves might meet them. The human play gets few rollouts, so that it is unlikely to match a zero bot's plays, and it is a good play, since it is necessary to control for the quality of play. Detecting cheating by the quality of play is rather different from detecting cheating by how unusual a play is for humans.

Edit: I could have omitted the move 2 example, but I did not want to omit any data. That would have been poor practice.

Posted: **Tue Jun 02, 2020 7:50 am**

Example 6.

Click Here To Show Diagram Code: [go]$$Bc Move 93 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . O . . X X X . X O O . . . | $$ | . . . X X O . X X O O O X X X O O O . | $$ | . . X , O . O . O X . . . . O X X O . | $$ | . . X O . . . . O . O a . . O . . X . | $$ | . . X . . X O . . . . . . X . X X . . | $$ | . . . . . . X . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . O . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . O . . . . . . . . . . . . O . . | $$ | . . . . . . . . . . . . . . X . . . . | $$ | . . . . . X . X X X . . . . X O . . . | $$ | . . O . O X X O X O X X . . . O . . . | $$ | . . . O . X O . O O O . . X O , . . . | $$ | . . O X X X O O O . b . . X O X O . . | $$ | . . O . . X O . . . O . X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Who played that attachment, who played the wedge?

The winrate difference is 0.1% (in favor of the human's play). Elf's play got 30k rollouts, the human's play got 395.

Elf played the wedge.

Elf's main variation is rather interesting. Bots can do local reading if they want to, it seems.

Click Here To Show Diagram Code: [go]$$Bcm93 Mainline $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . O . . X X X . X O O . . . | $$ | . . . X X O . X X O O O X X X O O O . | $$ | . . X , O . O . O X . . . . O X X O . | $$ | . . X O . . . . O . O . . . O . . X . | $$ | . . X . . X O . . . . . . X . X X . . | $$ | . . . . . . X . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . O . . | $$ | . . . , . . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . O . . . . . . . . . . . . O . . | $$ | . . . . . . . . . . . . . . X . . . . | $$ | . . . . . X . X X X . . . . X O . . . | $$ | . . O . O X X O X O X X . . . O . . . | $$ | . . . O . X O . O O O 8 . X O , . . . | $$ | . . O X X X O O O 4 1 2 . X O X O . . | $$ | . . O . . X O . 7 9 O 3 X . X O . . . | $$ | . . . . . . . . a 6 5 0 . X . . . . . | $$ ---------------------------------------[/go]

(B 103 at a, for a ko.)

could have played the sagari at 97. The wedge may be considered as a probe.

Posted: **Tue Jun 02, 2020 8:13 am**

Example 7.

Click Here To Show Diagram Code: [go]$$B Move 101 $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . O . . X X X . X O O . . . | $$ | . . . X X O . X X O O O X X X O O O . | $$ | . . X , O . O . O X . . . . O X X O . | $$ | . . X O . . a . O O O X . . O . . X . | $$ | . . X . . X O . . . . . . X . X X . . | $$ | . . . . . . X . . . . . . . . . . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . X O X . . . . . . . . . . . . O . . | $$ | . . X O O . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . O . . . . . . . . . . . . O . . | $$ | . . . . . . . . . . . . . . X . . . . | $$ | . . . . . X . X X X . . . . X O . . . | $$ | . . O . O X X O X O X X . . . O . . . | $$ | . . . O . X O . O O O . . X O , . . . | $$ | . . O X X X O O O . . . . X O X O . . | $$ | . . O . . X O . . . O b X . X O . . . | $$ | . . . . . . . . . . . . . X . . . . . | $$ ---------------------------------------[/go]

Who played the atari, who played the butt?

Winrate difference: 0.8%. Elf's play got 20k rollouts, the human's play got 125.

Elf played the butt. No ko this time.

Click Here To Show Diagram Code: [go]$$Bcm1 Mainline variation $$ --------------------------------------- $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . . . . O . . X X X . X O O . . . | $$ | . . . X X O . X X O O O X X X O O O . | $$ | . . X , O . O . O X . . . . O X X O . | $$ | . . X O . . . . O O O X . . O . . X . | $$ | . . X . 9 X O . . . . . . X . X X . . | $$ | . . . . . 8 X . . . . . . . . . . . . | $$ | . . 7 . . . . . . . . . . . . . . . . | $$ | . X O X . . . . . . . . . . . . O . . | $$ | . . X O O . . . . , . . . . . , . . . | $$ | . . . . . . . . . . . . . . . . . . . | $$ | . . . O . . . . . . . . . . . . O . . | $$ | . . . . . . . . . . . . . . X . . . . | $$ | . . . . . X . X X X . . . . X O . . . | $$ | . . O . O X X O X O X X . . . O . . . | $$ | . . . O . X O . O O O . . X O , . . . | $$ | . . O X X X O O O 6 3 5 . X O X O . . | $$ | . . O . . X O . 4 . O 1 X . X O . . . | $$ | . . . . . . . . . . 2 . . X . . . . . | $$ ---------------------------------------[/go]

Black plays sente against White's bottom side group, and then takes the ponnuki on the left side.

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Re: On handling online cheating with AI

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