Re: [Computer-go] Project Leela Zero

Is this something LeelaZero might consider using? https://arxiv.org/pdf/1803.05407.pdf The last diagram is looking very impressive. It's not a game playing domain, but still. Maybe this averaging technique could help reduce the gap for a (relatively) low resource project like Leela. On Wed, Jan

Re: [Computer-go] Breakthrough: FineArt giving handicap against pro players

Ke Jie sacrificed himself to help the Nihon Kiin get off its high horse. :-) Congrats on your own results. > > ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Patterns and bad shape

On Mon, Apr 17, 2017 at 3:04 PM, David Wu wrote: > To some degree this maybe means Leela is insufficiently explorative in > cases like this, but still, why does the policy net not put H5 more than > 1.03%. After all, it's vastly more likely than 1% that that a good player

Re: [Computer-go] AlphaGo rollout nakade patterns?

Finally, somebody asks about the nature of those 8192 patterns.(pardon the Nature pun) ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] it's alphago

> Honestly I got a little frustrated that many people didn't think that > was AlphaGo. It was almost clear to me because I know the difficulty of > developing AlphaGo-like bots. > I feel with you. People seem to think that the Nature paper gave away the full recipe.

Re: [Computer-go] Chun-Hsun Chou 9p vs Yi-Min Hsieh 6p and DarkForest on ICIRA 2016 Tokyo

It's hard to tell, because bots don't really play outrageous moves anymore, especially not it good positions. My guess would be the shoulderhit at 41. ___ Computer-go mailing list Computer-go@computer-go.org

[Computer-go] Scoring Network

Has anyone ever tried to build a value network that is trained on finished positions? I admit that would be less awesome than what AlphaGo's value network has achieved. But reducing the task to status and scoring might help in endgame play. Generalizing this, there could be several value networks,

Re: [Computer-go] Creating the playout NN

> > The purpose is to see if there is some sort of "simplification" available > to the emerged complex functions encoded in the weights. It is a typical > reductionist strategy, especially where there is an attempt to converge on > human conceptualization. > > That's an interesting way to look at

Re: [Computer-go] Creating the playout NN

> > BTW, by improvement, I don't mean higher Go playing skill...I mean > appearing close to the same level of Go playing skill _per_ _move_ with far > less computational cost. It's the total game outcomes that will fall. > > For the playouts, you always need a relatively inexpensive computation.

Re: [Computer-go] Creating the playout NN

06-11 23:06 GMT+03:00 Stefan Kaitschick <stefan.kaitsch...@hamburg.de > >: > >> If I understood it right, the playout NN in AlphaGo was created by using >> the same training set as the one used for the large NN that is used in the >> tree. There would be an alternative

[Computer-go] Creating the playout NN

If I understood it right, the playout NN in AlphaGo was created by using the same training set as the one used for the large NN that is used in the tree. There would be an alternative though. I don't know if this is the best source, but here is one example: https://arxiv.org/pdf/1312.6184.pdf The

Re: [Computer-go] new challenge for Go programmers

Your lack of respect for task performance is misguided imo. Your preconceived notions of what intelligence is, will lead you astray. ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

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

The evaluation is always at least as deep as leaves of the tree. Still, you're right that the earlier in the game, the bigger the inherent uncertainty. One thing I don't understand: if the network does a thumbs up or down, instead of answering with a probability, what is the use of MSE? Why not

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

I always thought the same. But I don't think they tackled the decomposition problem directly. Achieving good(non-terminal) board evaluations must have reduced the problem. If you don't do full playouts, you get much less thrashing between independent problems. It also implies a useful static L

Re: [Computer-go] Number of Go positions computed at last

Good joke to render the solution as a board position. ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] NHK News "Bots will be admitted to pro tournaments"

According to a post in this forum it's 13*13. http://lifein19x19.com/forum/viewtopic.php?f=18=12553 ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Facebook Go AI

> I understand the idea, that long term prediction might lead to a > different optimum (but it should not lead to one with a higher one > step prediction rate: it might result in a stronger player with the > same prediction rate...), and might increase training speed, but hard > facts would be

Re: [Computer-go] Komi 6.5/7.5

I agree with Robert. 7 is still a hot candidate for all board sizes. Stefan ___ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] AMAF/RAVE + heavy playouts - is it save?

The name "Monte Carlo" strongly seems to suggest, that randomness it at the core of the method. And randomness does play a role. But what really happend in the shift to MC, was that bots didn't try to evaluate intermediate positions anymore. Instead, all game knowledge was put into selecting

Re: [Computer-go] CGF Open 2015 result

A thought about that impressive article: Move prediction has become the bot workhorse. But I have a question: how can those predictions work, without having a goal? The predictions are obviously purely shape based, so a 41% success rate is really pretty awesome. But that means that some positions,

Re: [Computer-go] codecentric Challenge 2015, Round 2

What Semeai is that? Do you mean the useless attack on the b center group? It looked to me simply like an aimless long endgame by Zen. But I would be interested to know, what Zen's specific problem was. ___ Computer-go mailing list

Re: [Computer-go] KGS bot tournaments - what are your opinions?

On Wed, Oct 7, 2015 at 7:21 PM, Dave Dyer wrote: > > How about handicapping the hardware based on time. Programs running > on more powerful hardware would get less time. > > I think that's a good idea. Programs could even aquire a time ranking, depending on their success in

Re: [Computer-go] KGS bot tournaments - what are your opinions?

> > 1. I do not see a way to do this but running on same hardware (e.g. > Amazon EC2 with graphic card). Even this is unfair, as programs might > be optimized to other configurations (cluster) > > First, there is the question is fairness is even desirable. But also, as you say, it is really

Re: [Computer-go] re comments on Life and Death

Robert, David Fotland has paid his dues on "truly intelligent" go programs. Maybe more than anybody else. I find your critique a little painful. Don't blame David, that the "stupid" monte carlo works so much better. ___ Computer-go mailing list

Re: [Computer-go] what do you (or does your computer) see?

When I click on a youtube video, I don't expect much. But I do expect to be at least marginally entertained. One more video of this "quality", and you will have lost me as a potential viewer. On Fri, Sep 4, 2015 at 5:31 AM, djhbrown . wrote: > > >

Re: [Computer-go] Dynamic komi - VBSC

The best contenders may be best for entirely different reasons. How do you compare a line that tries to bring down a huge group with a line that cautiously tries to optimize safe points. It's really hard to do. And whereever the dynamic komi lands, pedestrian variations may look great or totally

Re: [Computer-go] re comments on Life and Death

So far I have not criticised but asked questions. I am a great fan of the expert system approach because a) I have studied go knowledge a lot and see, in principle, light at the end of the tunnel, b) I think that "MC + expert system" or "only expert system" can be better than MC if the expert

Re: [Computer-go] Programme NAO to play Go and talk about it

The quantum leap in go computing was to discard all structures, to plan nothing, to evaluate nothing except final score. That has it's own limits - programmers wish they could get some separation of concerns implemented. But we're talking about going from beating pros with 4 stones to beating them

Re: [Computer-go] OT (maybe): Arimaa bot notably stronger

The funny thing is, that in computer go there are no goals, except winning. And therefore, the reason for a win cannot be determined. One crude measure might be to use stronger attacking moves in the playouts, when the winrate is low. unrelated: Does anyone know if the successful Arimaa bot

Re: [Computer-go] Arimaa bot notably stronger

On http://arimaa.com/arimaa/challenge/2015/showGames.cgi the games can be replayed. That's nice. Arimaa looks like a rather tedious game to actually play though. I have never played it, but the idea seems to be to take the opponents camel hostage at a sinkhole, and then go bossing around

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

But, I imagine this is more fuss than it is worth; the NN will be integrated into MCTS search, and I think the strong programs already have ways to generate ko threat candidates. Darren Do they? What would look like? Playing 2 moves in a row for the same side? I thought the programs naively

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

Let's be pragmatic - humans heavily use the information about the last move too. If they take a while, they don't need to know the last move of the opponent when reviewing a position, but when reading a tactical sequence the previous move in the sequence is essential piece of information.

Re: [Computer-go] Evaluation function through Deep Convolutional Neural Networks

To me, that's the core lesson of MCTS - take your hands off that evaluation button. On Sat, Jan 10, 2015 at 12:00 AM, Darren Cook dar...@dcook.org wrote: The discussion on move evaluation via CNNs got me wondering: has anyone tried to make an evaluation function with CNNs ? My first

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

Last move info is a strange beast, isn't it? I mean, except for ko captures, it doesn't really add information to the position. The correct prediction rate is such an obvious metric, but maybe prediction shouldn't be improved at any price. To a certain degree, last move info is a kind of

Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

Great work. Looks like the age of nn is here. How does this compare in computation time to a heavy MC move generator? One very minor quibble, I feel like a nag for even mentioning it: You write The most frequently cited reason for the difficulty of Go, compared to games such as Chess, Scrabble

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

That's pretty good looking for a pure predictor. Considering it has no specific knowledge about semeais, ladders, or ko threat situations... Switching out the pattern matcher (not the whole move generator) in an existing mc program, should be pretty straightforward. Even if the nn is a lot slower

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

A move generator, that always plays it's first choice, that can win games against Fuego? That smells like a possible game changer.(pardon the pun). Surely, programmers will take this workhorse, and put it before the MC cart. Stefan ___ Computer-go

Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

Finally, I am not a fan of NN in the MCTS architecture. The NN architecture imposes a high CPU burden (e.g., compared to decision trees), and this study didn't produce such a breakthrough in accuracy that I would give away performance. Is it really such a burden? Supporting the move

Re: [computer-go] Re: Open source real time Go server

As for other things we'd like to see improved, we could build a list. My pet peeve is the KGS score estimator, which is often wildly wrong. I've heard complaints about the implementation of the rules, and some have argued that it is not terribly bot-friendly. A good SE is a terribly difficult

Re: [computer-go] Re: Open source real time Go server

2010/1/19 terry mcintyre terrymcint...@yahoo.com: ( I recall a pro making such an observation; I was willing to accept his expertise on the matter. ) Any pro making such a comment at move 10 is just grand-standing. I have experienced pros making such comments too. You can let such a remark

Re: [computer-go] 13x13 human vs computer

9*9: 6 dan 19*19 :1 kyu 13*13 1 dan? not the expected interpolation :-) Looks like programing for a specific board size is important. Stefan - Original Message - From: David Fotland fotl...@smart-games.com To: 'computer-go' computer-go@computer-go.org Sent: Monday,

Re: [computer-go] Zen beats a 5-dan on KGS

Very nice. You just can't be cavalier tactically when you play a bot. Even though this was an even game, the human started playing handicap style after a few obviously weak bot moves. But the kind of tactical handicap play that w tried completely backfired against Zens strong tactical

[computer-go] one more look at the scoring function

Looking at the games on kgs, both ManyFaces and Zen are pretty decent at giving handicap, but still fairly weak at taking handicap. I think the problem remains, that they allow the opponent to close the gap too easily. Once the game is close they get much stronger, but by then they are in a real

Re: [computer-go] problem which current programs have difficulty solving

I wouldn't consider not solving this pathological. I think it's a pretty difficult problem. Without a problem warning most amateur players would miss it too. You can't force life and you can't force connection. The either-or is easy to miss. Stefan - Original Message - From: terry

Re: [computer-go] KCC won the 3rd UEC Cup

whether your bot thinks it's legal or not. I don't see how this is an indictment, the rules are what they are. For every player. It's not as if this was a little-known issue with Japanese rules. Mark On Sun, Nov 29, 2009 at 10:57 PM, Stefan Kaitschick stefan.kaitsch...@hamburg.de wrote: Crazy Stone

Re: [computer-go] Live broadcasting at UEC Cup

Another option would be double elimination. Drawbacks are that you first need to trim the field down to 16 contestants, and that it takes more rounds than single elimination. (aka ko) The advantage is that it retains the ko characteristic of everybody still in it can win it while lessening the

Re: [computer-go] KCC won the 3rd UEC Cup

Crazy Stone (CS) lost the first game due to a wrong ko setting. The opponent of CS played a superko violation which was legal under Japanese rules, and CS lost the game by a faul. The most devastating indictment against japanese rules I've seen so far. Stefan

Re: [computer-go] Re: Hahn system tournament and MC bots

No professional gambler, if he had the numbers laid out for him, would ever choose unoptimal play, not when he's playing for the long term. The computer, in the same way, would have to be modeled to maximize expected value. Nothing else makes sense. In a single game with high stakes, yes mindset

Re: [computer-go] Hahn system tournament and MC bots

If scoring matters, then instead of just estimating the winrate for a certain move, a bot has to estimate a komi/winrate function. As a shortcut, maybe a simoid scoring function will suddenly start to shine. But that really folds winrate and winning score into a single dimension. If that is too

Re: [computer-go] Re: Joseki Book

Ofcourse I can't say what a correct opening is. What I can say though, is that if bots are onto something with their strange openings, at this point it's by accident. It is not by accident, it is consistent with what the bot can read. What I mean is that it may well be legitimate to play

Re: [computer-go] Go Programming Language

Has anyone tried programming Go in the Go Programming Language? http://golang.org/ And the result would be a gogo? hurray! go gogo! ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/

Re: [computer-go] Re: Joseki Book

Characterising the style quickly, it can start the first few moves at almost any intersections 3rd line and above ... Ignorants call the early moves random but it is only because they lack an understanding of their reasoning ... The first statement is a pretty good definition of random.

Re: [computer-go] Re: Joseki Book

I admit that I was narrowly parsing words. I do like your bermuda triangle :-) Stefan - Original Message - From: Robert Jasiek jas...@snafu.de To: computer-go computer-go@computer-go.org Sent: Wednesday, November 11, 2009 2:57 PM Subject: Re: [computer-go] Re: Joseki Book Stefan

Re: [computer-go] Joseki Book

True, but at the moment we're just interested in getting Orego to play ANY joseki, i.e., a reasonable move in some corner, rather than a disastrous tenuki. Finding the right joseki will be future work. (Orego also has a small fuseki book, which we're working to expand.) On an intermediate

Re: [computer-go] Re: Joseki Book

Ofcourse I can't say what a correct opening is. What I can say though, is that if bots are onto something with their strange openings, at this point it's by accident. They really do underestimate the chances of an invader. That goes for moyo parachute invasions as well as corner invasions. I think

Re: [SPAM] Re: [computer-go] First ever win of a computer against apro 9P as black (game of Go, 9x9).

MCTS avoids evaluation. That is its main trick. It also avoids subproblems like the plague. Atleast sofar. I think you are absolutely right that in the end a program will need to be able to define subproblems, their local status, and the conditions that will change that status. The current

Re: [computer-go] First ever win of a computer against a pro 9P asblack (game of Go, 9x9).

c) There was one non-blitz game (45 minutes per side). MoGo was unlucky as it was black, but it nonetheless won the game. This game is enclosed. All games can be found on KGS (account nutngo) Congratulations. b) MoGo already won a game as black, against Catalin Taranu, but I guess

Re: [computer-go] MC hard positions for MCTS

I would add invasions. This is especially obvious when the position cries for a san-san invasion. Nakade may be a part of it. But the biggest problem is that the path to life/ko is very narrow. The defender has many useful moves and the invader has few. So MCTS will falsely judge invadable areas

Re: [computer-go] MC hard positions for MCTS

But, has anyone gathered stats on positions, from real games, that require precise play by the defender/attacker/both/neither? Is defending really easier than attacking? Darren Who is the defender? One side is defending his territory, the other side is defending his group. I think the

Re: [computer-go] MC hard positions for MCTS

This causes what I call the horizon effect which prevents the tree exploration to work properly - the moment the tree finds a sequence that unbiases the simulations, it is horrified by the bad results [*] and switches to a different branch, until it finds the same; thus, the bot pushes the

Re: [computer-go] First ever win of a computer against a pro 9P asblack (game of Go, 9x9).

29, turning into a won ko, really is a great way to play. It would be interesting to know if MoGo perceived itself to be on the home stretch here. So it would be great to have the bots win rate estimations as sgf comments. Stefan - Original Message - From: Olivier Teytaud To:

Re: [computer-go] Pattern matching

Very Cool. The mysterious part was more interesting than programming go. That seemed almost impossible. Stefan - Original Message - From: Mark Boon tesujisoftw...@gmail.com To: computer-go computer-go@computer-go.org Sent: Wednesday, October 14, 2009 11:23 PM Subject: Re:

Re: [computer-go] Generalizing RAVE

Here's a suggestion to extend RAVE to better handle it: There are 20 points within keima distance of any point not close to the edge.(5*5 without the corners) When RAVE values are backed up, they are put into the category defined by the previous opponents move. (21 categories, 20 + other. All

[computer-go] public test suite

Every now and then somebody submits an interesting 9*9 problem, usually rendered in X and O. Wouldn't it be great to have a public suite, lets say a directory with black to play and win sgf problems. For quick testing there should be only one correct first move. There could also be

Re: [computer-go] Generalizing RAVE

Stefan Kaitschick wrote: Here's a suggestion to extend RAVE to better handle it: There are 20 points within keima distance of any point not close to the edge.(5*5 without the corners) When RAVE values are backed up, they are put into the category defined by the previous opponents move. (21

Re: [computer-go] Generalizing RAVE

MCTS, even though it walks to the end of the earth, has it's own horizon effect. The name is more fitting for depth-limited alpha-beta search ofcourse. It's a kind of procrastination. Finding a lot of useless things to do before admitting an undesirable, but unavoidable consequence. Even if a

Re: [computer-go] Generalizing RAVE

It is exactly the same as my thought. I also have tried CRAVE, but the results were worse than normal RAVE. While RAVE is a very efficient algorithm, it strongly limits scalability of the program. It typically makes a fatal mistake in the position that the order of moves are important. We

[computer-go] string criticality?

Rémi Coulom offers a formula for the criticality of point x. (Criticality: a Monte-Carlo Heuristic for Go Programs) Criticality being a measure of how important holding x is for winning. c(x) = v(x)/N - (w(x)/N * W/N + b(x)/N * B/N) N: number of playouts W/B: playouts won by white/black

Re: [computer-go] string criticality?

Few months ago I tested it without success. String criticality seems a nice idea, but how should it be implemented? Just giving high priority to the liberties does not work, because that cannot be distinguished from the simple dame-filling. Can you suggest a concrete formula? -- Yamato a shot

Re: [computer-go] string criticality?

-go.org Sent: Monday, September 14, 2009 5:08 PM Subject: Re: [computer-go] string criticality? Stefan Kaitschick wrote: something like this(a little Bayes): P(C|X) = P(X|C) * P(C) / P(X) P(C|X): chance that the string will be captured if x is played by any side P(C): string_captured_count / N P

Re: [computer-go] Stone-Age

Stone-Age - spooky concept :-) I suppose it has some relationship to generally lighter playouts deeper in the tree. Have you experimented some more with this? Perhaps the cutoff point should be somewhere in the future though, moving towards the present as the game progesses. Otherwise you

Re: [computer-go] Stone-Age

at 10:55 AM, Stefan Kaitschick stefan.kaitsch...@hamburg.de wrote: Stone-Age - spooky concept :-) I suppose it has some relationship to generally lighter playouts deeper in the tree. Have you experimented some more with this? No, I didn't have time to explore this further. Perhaps the cutoff

Re: [computer-go] (two won semiais = lost game?) semiais - expulsion from mc paradise

pattern will cause one side to fill its own liberty. David -Original Message- From: computer-go-boun...@computer-go.org [mailto:computer-go- boun...@computer-go.org] On Behalf Of Stefan Kaitschick Sent: Monday, September 07, 2009 2:36 AM To: computer-go Subject: [computer-go] two won semiais

[computer-go] two won semiais = lost game?

I have a general question: how good are the current information gathering mechanisms in the mc tree to insure that advantagous semiais are actually won? Random playouts will surely give the side with more libs the advantage, but to what degree? Lets say the leading side wins the semiai in 2/3

Re: [computer-go] two won semiais = lost game?

It is obvious that the current mechanism is bad. And another problem on the wrong evaluation is the amplification of the error. When there are unresolved life/death or semeais on the board, typical MC programs become weak because of the instability of the simulations. I think that we need a

Re: [computer-go] Dynamic komi at high handicaps

Good work Ingo. But why should it be near 50%? If it is, the komi is too large.(if giving handicap) You just have to reserve some thinking time for reruns, in case the komi estimate from the last move doesn't fit anymore. Stefan (ii) Also on 13x13 board dynamic komi seems to help, although

Re: [computer-go] Dynamic komi at high handicaps

One last rumination on dynamic komi: The main objection against introducing dynamic komi is that it ignores the true goal of winning by half a point. The power of the win/loss step function as scoring function underscores the validity of this critique. And yet, the current behaviour of mc bots,

Re: [computer-go] MoGo - ManyFaces

13 games were played and the total score was 8-5 for CzechBot. I wonder how would they play if on even grounds. The general game pattern was the usual wild middlegame wrestling typical of MC, with CzechBot usually getting large edge initially (70% winning probability and seemingly unshakeable

Re: [computer-go] Dynamic komi at high handicaps

Maybe I should ask first, for clarity sake, is MCTS performance in handicap games currently a problem? Mark Yes, it's a big problem. And thats not a matter of opinion. MC bots, leading a game by a large margin, will give away their advantage lighly except for the last half point. Even on a

Re: [computer-go] Dynamic komi at high handicaps

Modeling the opponents mistakes is indeed an alternative to introducing komi. But it would have to be a lot more exact than simply rolling the dice or skipping a move here and there. Successful opponent modeling would implement the overplay school of thought - playing tactically refutable

Re: [computer-go] Dynamic komi at high handicaps

What a bot does with its playouts in a handicap situation is to essentially try to beat itself, despite the handicap. And in this situation the bot reacts in a very human way, it becomes despondend. Adjusting the komi dynamically shifts the goal from winning to catching up quickly enough. I

Re: [computer-go] Dynamic komi at high handicaps

What seems difficult to me however is to devise a reasonable way to decrease this komi as the game progresses Certainly that is the main problem. But the main considerations are not so hard to find 1. Win rate of the best move. 2. How far has the game progressed 3. deviation between the win

Re: [computer-go] Dynamic komi at high handicaps

For instance I am sure he will not sit merrily by and watch his opponent consolidate a won game just so that he can have a respectable but losing score.Dynamic komi of course does not address that at all. This seems self evident, but it may actually be a treacherous conclusion. Dynamic

Re: [computer-go] Re: Dynamic komi in commercial programs

I think the most promising field for using dynamic komi is in low handicap play. Because the main strategic principle for w in low handicap play is patience, which is easily and naturally modeled by a declining komi. For high handicaps the main strategic principle is light play, which is an

Re: [computer-go] Re: Dynamic komi in commercial programs

- Original Message - From: Dave Dyer dd...@real-me.net To: computer-go computer-go@computer-go.org Sent: Saturday, July 11, 2009 10:54 PM Subject: [computer-go] Re: Dynamic komi in commercial programs If you are in a lost position, good play is play that maximizes the probability

Re: [computer-go] Scoring - step function or sigmoid function?

Thinking about why... In a given board position moves can be grouped into sets: the set of correct moves, the set of 1pt mistakes, 2pt mistakes, etc. Let's assume each side has roughly the same number of moves each in each of these groupings. If black is winning by 0.5pt with perfect play,

Re: [computer-go] Scoring - step function or sigmoid function?

My conclusion was that the winning percentage is more than just an estimate of how likely the player is to win. It is in fact a crude estimator of the final score. Going back to your original comment, when choosing between move A that leads to a 0.5pt win, and move B that leads to a 100pt win,

[computer-go] Scoring - step function or sigmoid function?

It seems to be surprisingly difficult to outperform the step function when it comes to mc scoring. I know that many surprises await the mc adventurer, but completely discarding the final margin of victory just can't be optimal. The sigmoid function can be tinkered with ofcourse, by making its