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 Post subject: Re: Easy to set up Go AI?
Post #41 Posted: Sat Aug 18, 2018 11:50 am 
Gosei

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John Fairbairn wrote:
Quote:
put your "un" in brackets as I think it was a typo.

It wasn't a typo though of course I may have made a mistake in other ways. My thinking was that as the board fills up and there are fewer moves to the end of the game (i.e. there is more information), it is possible to make more moves that are reliable (even if not always the best).

Agreed; that's I would have expected you to say "as the game progresses so does the certainty" rather than "as the game progresses so does the uncertainty", but if we both agree then it doesn't matter.

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Quote:
Winrate is how probable LZ thinks it is that Black would win if it were allowed to take over the game and play both sides from here to the end. So certainly if move A has a higher winrate than move B you can infer that LZ thinks that move A is better, without having to worry about exactly what each number means.

Yes, I understand that. But that is just when comparing A, B, C at the same move. What I was postulating was that, nearer the end of the game, the winrate for each of A, B and C is likely to be higher because of the "certainty" I already alluded to. That is, winrate at move 100 implies something a bit different from winrate at move 50. I suppose what I am really referring to is the winrate graph and am implying that its trend is not really telling us anything. The only parts that seem hugely significant are when the direction of the line changes drastically.

I guess there are two ways of looking at it. If a winrate of, say, 60% slowly climbs over the course of the game, it means both
  • No one made any big mistakes over the rest of the game
  • Black's chance of converting his advantage kept growing (since the amount of time left to turn it around decreased)
If you're looking for mistakes you don't want to freak out too much about the trend, but it does mean something.

These sorts of winrate graphs have become popular in sports analysis over the last 10+ years so I am used to seeing the same sort of shape when, for example, a baseball team takes an early one-run lead and nurses it to victory; there is definitely a much different feeling in the ninth inning of a 3-2 game than the third.

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 Post subject: Re: Easy to set up Go AI?
Post #42 Posted: Sat Aug 18, 2018 12:04 pm 
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dfan wrote:
Terminal positions in the tree search are actually evaluated by the game rules. However, the position is not considered terminal until both players have passed. The engines do know how to pass but I don't know how often it comes up in the tree search. Since they all use Chinese rules there is no penalty for continuing to play on for a while after the dame are filled.

As an illustration, here is a recent self play of LZ (network #166) with resign disabled :

http://zero.sjeng.org/view/3ad105e1870e ... viewer=wgo

Black pass only because it has no legal moves remaining.

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 Post subject: Re: Easy to set up Go AI?
Post #43 Posted: Sat Aug 18, 2018 1:15 pm 
Honinbo

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dfan wrote:
Bill Spight wrote:
Bill Spight wrote:
It is my impression that the standard value networks are trained with 7.5 komi, so that komi is implied. My guess, however, is that komi must be explicitly supplied for the Monte Carlo playout results. IIUC, the winrates are the average of the two. What happens if the actual komi, zero in this case, is given to the Monte Carlo calculations?

Tryss wrote:
There is no Monte Carlo random simulations, it's just the value network that estimate the position at each leaf


So that's different from AlphaGo Zero, IIRC?

AlphaGo did actual Monte Carlo simulations to the end of the game. AlphaGo Zero and all its descendants (AlphaZero, Leela Zero, ELF OpenGo, etc.) do not, despite continuing to use the (now misleading) term "Monte Carlo Tree Search".


Thanks. But Leela still uses Monte Carlo playouts, right? I noticed "MCWR" in Bojanic's analysis of the Metta-Ben David game, and figured that stood for Monte Carlo win rate. That's one reason for my confusion on that issue.

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And the value network doesn't do any actual counting, even if the end of play is reached? It just kind of "knows" who won? If so, that could explain the recent peculiar end of game behavior of Zen in one game.

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Terminal positions in the tree search are actually evaluated by the game rules. However, the position is not considered terminal until both players have passed. The engines do know how to pass but I don't know how often it comes up in the tree search. Since they all use Chinese rules there is no penalty for continuing to play on for a while after the dame are filled.


Right. So even though the game in question was played by territory rules with a 6.5 komi, Zen had no way of knowing that, and could not tell that making an unnecessary protective play would lose the game. It also produced a very peculiar win rate that, again, seemed to me like it was based upon semi-random playouts. ;)

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 Post subject: Re: Easy to set up Go AI?
Post #44 Posted: Sat Aug 18, 2018 1:17 pm 
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John Fairbairn wrote:
My interpretation of the last two points, at east in this game, is that LZ preferred solidity.
For a different impression, you can try the first Go-Kitani game from Kamakura ("The one with the nosebleed"). I tried this one out with a much older version of LZ, so it may have changed - but at the time it really disliked Kitani's slow and solid moves in the beginning.

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 Post subject: Re: Easy to set up Go AI?
Post #45 Posted: Sat Aug 18, 2018 1:37 pm 
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bernds wrote:
John Fairbairn wrote:
My interpretation of the last two points, at east in this game, is that LZ preferred solidity.
For a different impression, you can try the first Go-Kitani game from Kamakura ("The one with the nosebleed"). I tried this one out with a much older version of LZ, so it may have changed - but at the time it really disliked Kitani's slow and solid moves in the beginning.

Seeing as Kitani didn't have to give komi, whereas LZ as black does, it's possible Kitani's moves were good for a no komi game if they weren't haemorrhaging points at too fast a rate, which would be on average (7.5 points / number of black moves in game) per move. But I guess they were, and checking on no komi LZ157 it says white was winning by move 26 (the kosumi to enclose bottom right). The ignored push on the 3 stones was the biggest mistake, a -11% (where black starts at 65%). It also didn't like quite a few of Go's moves, basically anything that didn't follow the order first empty corners, then shimaris/approaches to 3-4s, then do other things.


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 Post subject: Re: Easy to set up Go AI?
Post #46 Posted: Sat Aug 18, 2018 2:00 pm 
Honinbo

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Tryss wrote:
dfan wrote:
Terminal positions in the tree search are actually evaluated by the game rules. However, the position is not considered terminal until both players have passed. The engines do know how to pass but I don't know how often it comes up in the tree search. Since they all use Chinese rules there is no penalty for continuing to play on for a while after the dame are filled.

As an illustration, here is a recent self play of LZ (network #166) with resign disabled :

http://zero.sjeng.org/view/3ad105e1870e ... viewer=wgo

Black pass only because it has no legal moves remaining.


Thanks. ;)

I note that Black made a moyo on the bottom side to center. :)

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 Post subject: Re: Easy to set up Go AI?
Post #47 Posted: Sun Aug 19, 2018 4:01 am 
Gosei

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Bill Spight wrote:
dfan wrote:
AlphaGo did actual Monte Carlo simulations to the end of the game. AlphaGo Zero and all its descendants (AlphaZero, Leela Zero, ELF OpenGo, etc.) do not, despite continuing to use the (now misleading) term "Monte Carlo Tree Search".

Thanks. But Leela still uses Monte Carlo playouts, right? I noticed "MCWR" in Bojanic's analysis of the Metta-Ben David game, and figured that stood for Monte Carlo win rate. That's one reason for my confusion on that issue.

Leela and Crazy Stone (from 2016 on) are based on AlphaGo and perform Monte Carlo playouts to the end of the game.

Leela Zero and ELF OpenGo are based on AlphaGo Zero and only expand one tree node at a time.

I wish very much that everyone involved had chosen better names.

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 Post subject: Re: Easy to set up Go AI?
Post #48 Posted: Sun Aug 19, 2018 2:58 pm 
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dfan wrote:
Leela Zero and ELF OpenGo are based on AlphaGo Zero and only expand one tree node at a time.

I wish very much that everyone involved had chosen better names.


As I understand it, the tree search is still probabilistic, since the nodes to expand are selected according to the probabilities given by the policy net. Therefore it is still a 'Monte Carlo' way of tree search?

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 Post subject: Re: Easy to set up Go AI?
Post #49 Posted: Sun Aug 19, 2018 4:14 pm 
Gosei

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zermelo wrote:
dfan wrote:
Leela Zero and ELF OpenGo are based on AlphaGo Zero and only expand one tree node at a time.

As I understand it, the tree search is still probabilistic, since the nodes to expand are selected according to the probabilities given by the policy net. Therefore it is still a 'Monte Carlo' way of tree search?

Node expansion in AlphaGo Zero is deterministic. At each iteration it chooses the "best" node, which is chosen by a combination of the value network, values propagating up the tree from descendant nodes, the policy network, and a exploration factor (where nodes that have been visited less get a bonus). The search algorithm continues to move to the node with the highest score in a greedy manner until it hits a leaf.

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 Post subject: Re: Easy to set up Go AI?
Post #50 Posted: Sun Aug 19, 2018 5:01 pm 
Honinbo

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dfan wrote:
Node expansion in AlphaGo Zero is deterministic. At each iteration it chooses the "best" node, which is chosen by a combination of the value network, values propagating up the tree from descendant nodes, the policy network, and a exploration factor (where nodes that have been visited less get a bonus). The search algorithm continues to move to the node with the highest score in a greedy manner until it hits a leaf.


Best first search. Just like the old days. :cool: ;)

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