Re: [Computer-go] Alphago and solving Go

[David Doshay]

Yes, that zeroth order number (the one you get to without any thinking about 
how the game’s rules affect the calculation) is outdated since early last year 
when this result gave us the exact number of legal board positions:

https://tromp.github.io/go/legal.html 

So, a complete game tree for 19x19 Go would contain about 2.08 * 10^170 unique 
nodes (see the paper for all 171 digits) but some number of duplicates of those 
nodes for the different paths to each legal position. 

In an unfortunate bit of timing, it seems that many people missed this result 
because of the Alpha Go news.

Cheers,
David G Doshay

ddos...@mac.com





> On 6, Aug 2017, at 3:17 PM, Gunnar Farnebäck  wrote:
> 
> On 08/06/2017 04:39 PM, Vincent Richard wrote:
>> No, simply because there are way to many possibilities in the game, roughly 
>> (19x19)!
> 
> Can we lay this particular number to rest? Not that "possibilities in the 
> game" is very well defined (what does it even mean?) but the number of 
> permutations of 19x19 points has no meaningful connection to the game of go 
> at all, not even "roughly".
> 
> /Gunnar
> ___
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> http://computer-go.org/mailman/listinfo/computer-go

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Re: [Computer-go] Alphago and solving Go

[Gunnar Farnebäck]

On 08/06/2017 04:39 PM, Vincent Richard wrote:
No, simply because there are way to many possibilities in the game, 
roughly (19x19)!


Can we lay this particular number to rest? Not that "possibilities in 
the game" is very well defined (what does it even mean?) but the number 
of permutations of 19x19 points has no meaningful connection to the game 
of go at all, not even "roughly".


/Gunnar
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Re: [Computer-go] Alphago and solving Go

[Álvaro Begué]

Eventually exploring the entire tree is what I would call "mathematically
sound", meaning that given enough time the algorithm is guaranteed to play
optimally. I would reserve "brute force" for algorithms that simply search
every possible variant exhaustively, like John Tromp's connect 4 program
Fhourstones does [very well, I may add].

But I do smell troll too, so I'll stop here. Enough feeding.

Álvaro.



On Sun, Aug 6, 2017 at 4:08 PM, Brian Sheppard via Computer-go <
computer-go@computer-go.org> wrote:

> Possibly you are answering a different question than the one posed?
> Possibly your interpretation is the one actually intended. I don’t know,
> and maybe you could be right about what was being asked.
>
>
>
> I do know the semantics of brute force, though, which you quoted below.
>
>
>
> Note that “brute force” != unintelligent. Inevitably, every brute force
> algorithm will incorporate intelligent heuristics. Consider the evolution
> of minimax, for example, via alpha-beta, selective extensions, LMR, etc.
>
>
>
>
>
>
>
> *From:* Steven Clark [mailto:steven.p.cl...@gmail.com]
> *Sent:* Sunday, August 6, 2017 2:52 PM
> *To:* Brian Sheppard 
> *Cc:* computer-go 
>
> *Subject:* Re: [Computer-go] Alphago and solving Go
>
>
>
> This is semantics. Yes, in the limit of infinite time, it is brute-force.
> Meanwhile, in the real world, AlphaGo chooses to balance its finite time
> budget between depth & width. The mere fact that the CNN policy network
> generates a score for each coordinate on the board in a given position,
> does not mean that all of those nodes will be expanded in any reasonable
> scenario.
>
>
>
> On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard  wrote:
>
> I understand why most people are saying that AlphaGo is not brute force,
> because it appears to be highly selective. But MCTS is a full width search.
> Read the AlphaGo papers, as one of the other respondents (rather
> sarcastically) suggested: AlphaGo will eventually search every move at
> every node.
>
>
>
> MCTS has the appearance of a selective search because time control
> terminates search while the tree is still ragged. In fact, it will search
> every continuation an infinite number of times.
>
>
>
> In order to have high performance, an MCTS implementation needs to search
> best moves as early as possible in each node. It is in this respect that
> AlphaGo truly excels. (AlphaGo also excels at whole board evaluation, but
> that is a separate topic.)
>
>
>
>
>
> *From:* Steven Clark [mailto:steven.p.cl...@gmail.com]
> *Sent:* Sunday, August 6, 2017 1:14 PM
> *To:* Brian Sheppard ; computer-go <
> computer-go@computer-go.org>
> *Subject:* Re: [Computer-go] Alphago and solving Go
>
>
>
> Why do you say AlphaGo is brute-force? Brute force is defined as: "In
> computer science, brute-force search or exhaustive search, also known as
> generate and test, is a very general problem-solving technique that
> consists of *systematically enumerating all possible candidates* for the
> solution and checking whether each candidate satisfies the problem's
> statement."
>
>
>
> The whole point of the policy network is to avoid brute-force search, by
> reducing the branching factor...
>
>
>
> On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go <
> computer-go@computer-go.org> wrote:
>
> Yes, AlphaGo is brute force.
>
> No it is impossible to solve Go.
>
> Perfect play looks a lot like AlphaGo in that you would not be able to
> tell the difference. But I think that AlphaGo still has 0% win rate against
> perfect play.
>
>
>
> My own best guess is that top humans make about 12 errors per game. This
> is estimated based on the win rate of top pros in head-to-head games. The
> calculation starts by assuming that Go is a win at 6.5 komi for either
> Black (more likely) or White, so a perfect player would win 100% for Black.
> Actual championship caliber players win 51% to 52% for Black. In 9-dan play
> overall, I think the rate is 53% to 54% for Black. Then you can estimate
> how many errors each player has to make to bring about such a result. E.g.,
> If players made only one error on average, then Black would win the vast
> majority of games, so they must make more errors. I came up with 12 errors
> per game, but you can reasonably get other numbers based on your model.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
> Behalf Of *Cai Gengyang
> *Sent:* Sunday, August 6, 2017 9:49 AM
> *To:* computer-go@computer-go.org
> *Subject:* [Computer-go] Alphago and solving Go
>
>
>
> Is Alphago brute force search?
>
> Is it possible to solve Go for 19x19 ?
>
> And what does perfect play in Go look like?
>
> How far are current top pros from perfect play?
>
>
>
> Gengyang
>
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> 

Re: [Computer-go] Alphago and solving Go

[Brian Sheppard via Computer-go]

Possibly you are answering a different question than the one posed? Possibly 
your interpretation is the one actually intended. I don’t know, and maybe you 
could be right about what was being asked.

 

I do know the semantics of brute force, though, which you quoted below.

 

Note that “brute force” != unintelligent. Inevitably, every brute force 
algorithm will incorporate intelligent heuristics. Consider the evolution of 
minimax, for example, via alpha-beta, selective extensions, LMR, etc.

 

 

 

From: Steven Clark [mailto:steven.p.cl...@gmail.com] 
Sent: Sunday, August 6, 2017 2:52 PM
To: Brian Sheppard 
Cc: computer-go 
Subject: Re: [Computer-go] Alphago and solving Go

 

This is semantics. Yes, in the limit of infinite time, it is brute-force. 
Meanwhile, in the real world, AlphaGo chooses to balance its finite time budget 
between depth & width. The mere fact that the CNN policy network generates a 
score for each coordinate on the board in a given position, does not mean that 
all of those nodes will be expanded in any reasonable scenario.

 

On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard  > wrote:

I understand why most people are saying that AlphaGo is not brute force, 
because it appears to be highly selective. But MCTS is a full width search. 
Read the AlphaGo papers, as one of the other respondents (rather sarcastically) 
suggested: AlphaGo will eventually search every move at every node.

 

MCTS has the appearance of a selective search because time control terminates 
search while the tree is still ragged. In fact, it will search every 
continuation an infinite number of times.

 

In order to have high performance, an MCTS implementation needs to search best 
moves as early as possible in each node. It is in this respect that AlphaGo 
truly excels. (AlphaGo also excels at whole board evaluation, but that is a 
separate topic.)

 

 

From: Steven Clark [mailto:steven.p.cl...@gmail.com 
 ] 
Sent: Sunday, August 6, 2017 1:14 PM
To: Brian Sheppard  >; 
computer-go  >
Subject: Re: [Computer-go] Alphago and solving Go

 

Why do you say AlphaGo is brute-force? Brute force is defined as: "In computer 
science, brute-force search or exhaustive search, also known as generate and 
test, is a very general problem-solving technique that consists of 
systematically enumerating all possible candidates for the solution and 
checking whether each candidate satisfies the problem's statement."

 

The whole point of the policy network is to avoid brute-force search, by 
reducing the branching factor...

 

On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go 
 > wrote:

Yes, AlphaGo is brute force.

No it is impossible to solve Go.

Perfect play looks a lot like AlphaGo in that you would not be able to tell the 
difference. But I think that AlphaGo still has 0% win rate against perfect play.

 

My own best guess is that top humans make about 12 errors per game. This is 
estimated based on the win rate of top pros in head-to-head games. The 
calculation starts by assuming that Go is a win at 6.5 komi for either Black 
(more likely) or White, so a perfect player would win 100% for Black. Actual 
championship caliber players win 51% to 52% for Black. In 9-dan play overall, I 
think the rate is 53% to 54% for Black. Then you can estimate how many errors 
each player has to make to bring about such a result. E.g., If players made 
only one error on average, then Black would win the vast majority of games, so 
they must make more errors. I came up with 12 errors per game, but you can 
reasonably get other numbers based on your model.

 

Best,

Brian

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org 
 ] On Behalf Of Cai Gengyang
Sent: Sunday, August 6, 2017 9:49 AM
To: computer-go@computer-go.org  
Subject: [Computer-go] Alphago and solving Go

 

Is Alphago brute force search? 

Is it possible to solve Go for 19x19 ? 

And what does perfect play in Go look like? 

How far are current top pros from perfect play?

 

Gengyang


___
Computer-go mailing list
Computer-go@computer-go.org  
http://computer-go.org/mailman/listinfo/computer-go

 

 

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Re: [Computer-go] Alphago and solving Go

[Ian Davis]

Aren't you a little bit too old now to be troling this list?

On Sun, Aug 6, 2017 at 3:49 PM, Cai Gengyang  wrote:

> Is Alphago brute force search?
> Is it possible to solve Go for 19x19 ?
> And what does perfect play in Go look like?
> How far are current top pros from perfect play?
>
> Gengyang
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
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Re: [Computer-go] Alphago and solving Go

[Brian Sheppard via Computer-go]

If AlphaGo actually used hard (e.g. permanent) pruning, then it would not be 
brute force. But it doesn’t operate that way, so it is brute force.

 

BTW, AlphaGo’s papers reports benefiting from search and RAVE. That suggests 
that hard pruning is a risky business.

 

From: David Wu [mailto:lightvec...@gmail.com] 
Sent: Sunday, August 6, 2017 2:54 PM
To: Brian Sheppard ; computer-go@computer-go.org
Cc: Steven Clark 
Subject: Re: [Computer-go] Alphago and solving Go

 

Saying in an unqualified way that AlphaGo is brute force is wrong in the spirit 
of the question. Assuming AlphaGo uses a typical variant of MCTS, it is 
technically correct. The reason it's technically correct uninteresting is 
because the bias introduced by a policy net is so extreme that it might as well 
be a selective search. 

 

Or put another way, imagine one were to set a threshold on the policy net 
output past a certain point in the tree such that moves below the threshold 
would be hard-pruned, and that threshold were set to a level that would prune, 
say, 70% of the legal moves in an average position. In technical sense, the 
search would no longer be full-width, and therefore it would suddenly become 
"not brute force" according to the definition earlier in the thread. But this 
distinction is not very useful, because moves in the tree that fall below such 
a threshold would receive zero simulations under any reasonable time controls 
anyways, so there would be no practical observable difference in the program's 
search or its play.

 

So - spirit of the question - no AlphaGo is not brute force, its search is 
selective to an extreme due to the policy net, the vast majority of 
possibilities will never be in practice given any attention or time whatsoever.

 

Technical answer - yes, AlphaGo is brute force, in that in the limit of having 
enormously vastly many more orders of magnitude of search time than we would 
ever devote to it and unbounded memory, it will theoretically eventually search 
everything (maybe, it would still depend on the actual details of its 
implementation).

 

 

On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard via Computer-go 
 > wrote:

I understand why most people are saying that AlphaGo is not brute force, 
because it appears to be highly selective. But MCTS is a full width search. 
Read the AlphaGo papers, as one of the other respondents (rather sarcastically) 
suggested: AlphaGo will eventually search every move at every node.

 

MCTS has the appearance of a selective search because time control terminates 
search while the tree is still ragged. In fact, it will search every 
continuation an infinite number of times.

 

In order to have high performance, an MCTS implementation needs to search best 
moves as early as possible in each node. It is in this respect that AlphaGo 
truly excels. (AlphaGo also excels at whole board evaluation, but that is a 
separate topic.)

 

 

From: Steven Clark [mailto:steven.p.cl...@gmail.com 
 ] 
Sent: Sunday, August 6, 2017 1:14 PM
To: Brian Sheppard  >; 
computer-go  >
Subject: Re: [Computer-go] Alphago and solving Go

 

Why do you say AlphaGo is brute-force? Brute force is defined as: "In computer 
science, brute-force search or exhaustive search, also known as generate and 
test, is a very general problem-solving technique that consists of 
systematically enumerating all possible candidates for the solution and 
checking whether each candidate satisfies the problem's statement."

 

The whole point of the policy network is to avoid brute-force search, by 
reducing the branching factor...

 

On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go 
 > wrote:

Yes, AlphaGo is brute force.

No it is impossible to solve Go.

Perfect play looks a lot like AlphaGo in that you would not be able to tell the 
difference. But I think that AlphaGo still has 0% win rate against perfect play.

 

My own best guess is that top humans make about 12 errors per game. This is 
estimated based on the win rate of top pros in head-to-head games. The 
calculation starts by assuming that Go is a win at 6.5 komi for either Black 
(more likely) or White, so a perfect player would win 100% for Black. Actual 
championship caliber players win 51% to 52% for Black. In 9-dan play overall, I 
think the rate is 53% to 54% for Black. Then you can estimate how many errors 
each player has to make to bring about such a result. E.g., If players made 
only one error on average, then Black would win the vast majority of games, so 
they must make more errors. I came up with 12 errors per game, but you can 
reasonably get other numbers based on your model.

 

Best,

Brian

Re: [Computer-go] Alphago and solving Go

[Brian Sheppard via Computer-go]

There is a definition of “brute force” on Wikipedia. Seems to me that MCTS 
fits. Deep Blue fits.

 

From: Álvaro Begué [mailto:alvaro.be...@gmail.com] 
Sent: Sunday, August 6, 2017 2:56 PM
To: Brian Sheppard ; computer-go 

Subject: Re: [Computer-go] Alphago and solving Go

 

It was already a stretch when people said that Deep Blue was a brute-force 
searcher. If we apply it to AlphaGo as well, the term just means nothing. 
Full-width and brute-force are most definitely not the same thing.

 

Álvaro.

 

 

 

On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard via Computer-go 
 > wrote:

I understand why most people are saying that AlphaGo is not brute force, 
because it appears to be highly selective. But MCTS is a full width search. 
Read the AlphaGo papers, as one of the other respondents (rather sarcastically) 
suggested: AlphaGo will eventually search every move at every node.

 

MCTS has the appearance of a selective search because time control terminates 
search while the tree is still ragged. In fact, it will search every 
continuation an infinite number of times.

 

In order to have high performance, an MCTS implementation needs to search best 
moves as early as possible in each node. It is in this respect that AlphaGo 
truly excels. (AlphaGo also excels at whole board evaluation, but that is a 
separate topic.)

 

 

From: Steven Clark [mailto:steven.p.cl...@gmail.com 
 ] 
Sent: Sunday, August 6, 2017 1:14 PM
To: Brian Sheppard  >; 
computer-go  >
Subject: Re: [Computer-go] Alphago and solving Go

 

Why do you say AlphaGo is brute-force? Brute force is defined as: "In computer 
science, brute-force search or exhaustive search, also known as generate and 
test, is a very general problem-solving technique that consists of 
systematically enumerating all possible candidates for the solution and 
checking whether each candidate satisfies the problem's statement."

 

The whole point of the policy network is to avoid brute-force search, by 
reducing the branching factor...

 

On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go 
 > wrote:

Yes, AlphaGo is brute force.

No it is impossible to solve Go.

Perfect play looks a lot like AlphaGo in that you would not be able to tell the 
difference. But I think that AlphaGo still has 0% win rate against perfect play.

 

My own best guess is that top humans make about 12 errors per game. This is 
estimated based on the win rate of top pros in head-to-head games. The 
calculation starts by assuming that Go is a win at 6.5 komi for either Black 
(more likely) or White, so a perfect player would win 100% for Black. Actual 
championship caliber players win 51% to 52% for Black. In 9-dan play overall, I 
think the rate is 53% to 54% for Black. Then you can estimate how many errors 
each player has to make to bring about such a result. E.g., If players made 
only one error on average, then Black would win the vast majority of games, so 
they must make more errors. I came up with 12 errors per game, but you can 
reasonably get other numbers based on your model.

 

Best,

Brian

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org 
 ] On Behalf Of Cai Gengyang
Sent: Sunday, August 6, 2017 9:49 AM
To: computer-go@computer-go.org  
Subject: [Computer-go] Alphago and solving Go

 

Is Alphago brute force search? 

Is it possible to solve Go for 19x19 ? 

And what does perfect play in Go look like? 

How far are current top pros from perfect play?

 

Gengyang


___
Computer-go mailing list
Computer-go@computer-go.org  
http://computer-go.org/mailman/listinfo/computer-go

 


___
Computer-go mailing list
Computer-go@computer-go.org  
http://computer-go.org/mailman/listinfo/computer-go

 

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Re: [Computer-go] Alphago and solving Go

[David Wu]

Saying in an unqualified way that AlphaGo is brute force is wrong in the
spirit of the question. Assuming AlphaGo uses a typical variant of MCTS, it
is technically correct. The reason it's technically correct uninteresting
is because the bias introduced by a policy net is so extreme that it might
as well be a selective search.

Or put another way, imagine one were to set a threshold on the policy net
output past a certain point in the tree such that moves below the threshold
would be hard-pruned, and that threshold were set to a level that would
prune, say, 70% of the legal moves in an average position. In technical
sense, the search would no longer be full-width, and therefore it would
suddenly become "not brute force" according to the definition earlier in
the thread. But this distinction is not very useful, because moves in the
tree that fall below such a threshold would receive zero simulations under
any reasonable time controls anyways, so there would be no practical
observable difference in the program's search or its play.

So - spirit of the question - no AlphaGo is not brute force, its search is
selective to an extreme due to the policy net, the vast majority of
possibilities will never be in practice given any attention or time
whatsoever.

Technical answer - yes, AlphaGo is brute force, in that in the limit of
having enormously vastly many more orders of magnitude of search time than
we would ever devote to it and unbounded memory, it will theoretically
eventually search everything (maybe, it would still depend on the actual
details of its implementation).


On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard via Computer-go <
computer-go@computer-go.org> wrote:

> I understand why most people are saying that AlphaGo is not brute force,
> because it appears to be highly selective. But MCTS is a full width search.
> Read the AlphaGo papers, as one of the other respondents (rather
> sarcastically) suggested: AlphaGo will eventually search every move at
> every node.
>
>
>
> MCTS has the appearance of a selective search because time control
> terminates search while the tree is still ragged. In fact, it will search
> every continuation an infinite number of times.
>
>
>
> In order to have high performance, an MCTS implementation needs to search
> best moves as early as possible in each node. It is in this respect that
> AlphaGo truly excels. (AlphaGo also excels at whole board evaluation, but
> that is a separate topic.)
>
>
>
>
>
> *From:* Steven Clark [mailto:steven.p.cl...@gmail.com]
> *Sent:* Sunday, August 6, 2017 1:14 PM
> *To:* Brian Sheppard ; computer-go <
> computer-go@computer-go.org>
> *Subject:* Re: [Computer-go] Alphago and solving Go
>
>
>
> Why do you say AlphaGo is brute-force? Brute force is defined as: "In
> computer science, brute-force search or exhaustive search, also known as
> generate and test, is a very general problem-solving technique that
> consists of *systematically enumerating all possible candidates* for the
> solution and checking whether each candidate satisfies the problem's
> statement."
>
>
>
> The whole point of the policy network is to avoid brute-force search, by
> reducing the branching factor...
>
>
>
> On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go <
> computer-go@computer-go.org> wrote:
>
> Yes, AlphaGo is brute force.
>
> No it is impossible to solve Go.
>
> Perfect play looks a lot like AlphaGo in that you would not be able to
> tell the difference. But I think that AlphaGo still has 0% win rate against
> perfect play.
>
>
>
> My own best guess is that top humans make about 12 errors per game. This
> is estimated based on the win rate of top pros in head-to-head games. The
> calculation starts by assuming that Go is a win at 6.5 komi for either
> Black (more likely) or White, so a perfect player would win 100% for Black.
> Actual championship caliber players win 51% to 52% for Black. In 9-dan play
> overall, I think the rate is 53% to 54% for Black. Then you can estimate
> how many errors each player has to make to bring about such a result. E.g.,
> If players made only one error on average, then Black would win the vast
> majority of games, so they must make more errors. I came up with 12 errors
> per game, but you can reasonably get other numbers based on your model.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
> Behalf Of *Cai Gengyang
> *Sent:* Sunday, August 6, 2017 9:49 AM
> *To:* computer-go@computer-go.org
> *Subject:* [Computer-go] Alphago and solving Go
>
>
>
> Is Alphago brute force search?
>
> Is it possible to solve Go for 19x19 ?
>
> And what does perfect play in Go look like?
>
> How far are current top pros from perfect play?
>
>
>
> Gengyang
>
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
>
>
> 

Re: [Computer-go] Alphago and solving Go

[Steven Clark]

This is semantics. Yes, in the limit of infinite time, it is brute-force.
Meanwhile, in the real world, AlphaGo chooses to balance its finite time
budget between depth & width. The mere fact that the CNN policy network
generates a score for each coordinate on the board in a given position,
does not mean that all of those nodes will be expanded in any reasonable
scenario.

On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard  wrote:

> I understand why most people are saying that AlphaGo is not brute force,
> because it appears to be highly selective. But MCTS is a full width search.
> Read the AlphaGo papers, as one of the other respondents (rather
> sarcastically) suggested: AlphaGo will eventually search every move at
> every node.
>
>
>
> MCTS has the appearance of a selective search because time control
> terminates search while the tree is still ragged. In fact, it will search
> every continuation an infinite number of times.
>
>
>
> In order to have high performance, an MCTS implementation needs to search
> best moves as early as possible in each node. It is in this respect that
> AlphaGo truly excels. (AlphaGo also excels at whole board evaluation, but
> that is a separate topic.)
>
>
>
>
>
> *From:* Steven Clark [mailto:steven.p.cl...@gmail.com]
> *Sent:* Sunday, August 6, 2017 1:14 PM
> *To:* Brian Sheppard ; computer-go <
> computer-go@computer-go.org>
> *Subject:* Re: [Computer-go] Alphago and solving Go
>
>
>
> Why do you say AlphaGo is brute-force? Brute force is defined as: "In
> computer science, brute-force search or exhaustive search, also known as
> generate and test, is a very general problem-solving technique that
> consists of *systematically enumerating all possible candidates* for the
> solution and checking whether each candidate satisfies the problem's
> statement."
>
>
>
> The whole point of the policy network is to avoid brute-force search, by
> reducing the branching factor...
>
>
>
> On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go <
> computer-go@computer-go.org> wrote:
>
> Yes, AlphaGo is brute force.
>
> No it is impossible to solve Go.
>
> Perfect play looks a lot like AlphaGo in that you would not be able to
> tell the difference. But I think that AlphaGo still has 0% win rate against
> perfect play.
>
>
>
> My own best guess is that top humans make about 12 errors per game. This
> is estimated based on the win rate of top pros in head-to-head games. The
> calculation starts by assuming that Go is a win at 6.5 komi for either
> Black (more likely) or White, so a perfect player would win 100% for Black.
> Actual championship caliber players win 51% to 52% for Black. In 9-dan play
> overall, I think the rate is 53% to 54% for Black. Then you can estimate
> how many errors each player has to make to bring about such a result. E.g.,
> If players made only one error on average, then Black would win the vast
> majority of games, so they must make more errors. I came up with 12 errors
> per game, but you can reasonably get other numbers based on your model.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
> Behalf Of *Cai Gengyang
> *Sent:* Sunday, August 6, 2017 9:49 AM
> *To:* computer-go@computer-go.org
> *Subject:* [Computer-go] Alphago and solving Go
>
>
>
> Is Alphago brute force search?
>
> Is it possible to solve Go for 19x19 ?
>
> And what does perfect play in Go look like?
>
> How far are current top pros from perfect play?
>
>
>
> Gengyang
>
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
>
>
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Re: [Computer-go] Alphago and solving Go

[Brian Sheppard via Computer-go]

I understand why most people are saying that AlphaGo is not brute force, 
because it appears to be highly selective. But MCTS is a full width search. 
Read the AlphaGo papers, as one of the other respondents (rather sarcastically) 
suggested: AlphaGo will eventually search every move at every node.

 

MCTS has the appearance of a selective search because time control terminates 
search while the tree is still ragged. In fact, it will search every 
continuation an infinite number of times.

 

In order to have high performance, an MCTS implementation needs to search best 
moves as early as possible in each node. It is in this respect that AlphaGo 
truly excels. (AlphaGo also excels at whole board evaluation, but that is a 
separate topic.)

 

 

From: Steven Clark [mailto:steven.p.cl...@gmail.com] 
Sent: Sunday, August 6, 2017 1:14 PM
To: Brian Sheppard ; computer-go 

Subject: Re: [Computer-go] Alphago and solving Go

 

Why do you say AlphaGo is brute-force? Brute force is defined as: "In computer 
science, brute-force search or exhaustive search, also known as generate and 
test, is a very general problem-solving technique that consists of 
systematically enumerating all possible candidates for the solution and 
checking whether each candidate satisfies the problem's statement."

 

The whole point of the policy network is to avoid brute-force search, by 
reducing the branching factor...

 

On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go 
 > wrote:

Yes, AlphaGo is brute force.

No it is impossible to solve Go.

Perfect play looks a lot like AlphaGo in that you would not be able to tell the 
difference. But I think that AlphaGo still has 0% win rate against perfect play.

 

My own best guess is that top humans make about 12 errors per game. This is 
estimated based on the win rate of top pros in head-to-head games. The 
calculation starts by assuming that Go is a win at 6.5 komi for either Black 
(more likely) or White, so a perfect player would win 100% for Black. Actual 
championship caliber players win 51% to 52% for Black. In 9-dan play overall, I 
think the rate is 53% to 54% for Black. Then you can estimate how many errors 
each player has to make to bring about such a result. E.g., If players made 
only one error on average, then Black would win the vast majority of games, so 
they must make more errors. I came up with 12 errors per game, but you can 
reasonably get other numbers based on your model.

 

Best,

Brian

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org 
 ] On Behalf Of Cai Gengyang
Sent: Sunday, August 6, 2017 9:49 AM
To: computer-go@computer-go.org  
Subject: [Computer-go] Alphago and solving Go

 

Is Alphago brute force search? 

Is it possible to solve Go for 19x19 ? 

And what does perfect play in Go look like? 

How far are current top pros from perfect play?

 

Gengyang


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Computer-go@computer-go.org  
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Re: [Computer-go] Alphago and solving Go

[Steven Clark]

Why do you say AlphaGo is brute-force? Brute force is defined as: "In
computer science, brute-force search or exhaustive search, also known as
generate and test, is a very general problem-solving technique that
consists of *systematically enumerating all possible candidates* for the
solution and checking whether each candidate satisfies the problem's
statement."

The whole point of the policy network is to avoid brute-force search, by
reducing the branching factor...

On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go <
computer-go@computer-go.org> wrote:

> Yes, AlphaGo is brute force.
>
> No it is impossible to solve Go.
>
> Perfect play looks a lot like AlphaGo in that you would not be able to
> tell the difference. But I think that AlphaGo still has 0% win rate against
> perfect play.
>
>
>
> My own best guess is that top humans make about 12 errors per game. This
> is estimated based on the win rate of top pros in head-to-head games. The
> calculation starts by assuming that Go is a win at 6.5 komi for either
> Black (more likely) or White, so a perfect player would win 100% for Black.
> Actual championship caliber players win 51% to 52% for Black. In 9-dan play
> overall, I think the rate is 53% to 54% for Black. Then you can estimate
> how many errors each player has to make to bring about such a result. E.g.,
> If players made only one error on average, then Black would win the vast
> majority of games, so they must make more errors. I came up with 12 errors
> per game, but you can reasonably get other numbers based on your model.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
> Behalf Of *Cai Gengyang
> *Sent:* Sunday, August 6, 2017 9:49 AM
> *To:* computer-go@computer-go.org
> *Subject:* [Computer-go] Alphago and solving Go
>
>
>
> Is Alphago brute force search?
>
> Is it possible to solve Go for 19x19 ?
>
> And what does perfect play in Go look like?
>
> How far are current top pros from perfect play?
>
>
>
> Gengyang
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
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Re: [Computer-go] Alphago and solving Go

[Brian Sheppard via Computer-go]

Yes, AlphaGo is brute force.

No it is impossible to solve Go.

Perfect play looks a lot like AlphaGo in that you would not be able to tell the 
difference. But I think that AlphaGo still has 0% win rate against perfect play.

 

My own best guess is that top humans make about 12 errors per game. This is 
estimated based on the win rate of top pros in head-to-head games. The 
calculation starts by assuming that Go is a win at 6.5 komi for either Black 
(more likely) or White, so a perfect player would win 100% for Black. Actual 
championship caliber players win 51% to 52% for Black. In 9-dan play overall, I 
think the rate is 53% to 54% for Black. Then you can estimate how many errors 
each player has to make to bring about such a result. E.g., If players made 
only one error on average, then Black would win the vast majority of games, so 
they must make more errors. I came up with 12 errors per game, but you can 
reasonably get other numbers based on your model.

 

Best,

Brian

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Cai 
Gengyang
Sent: Sunday, August 6, 2017 9:49 AM
To: computer-go@computer-go.org
Subject: [Computer-go] Alphago and solving Go

 

Is Alphago brute force search? 

Is it possible to solve Go for 19x19 ? 

And what does perfect play in Go look like? 

How far are current top pros from perfect play?

 

Gengyang

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Re: [Computer-go] Alphago and solving Go

[David Wu]

Actually, a better Go-God for handicap games would probably be one that
ignores score margin as long as it's behind and simply maximizes the
entropy measure for the lowest-entropy proof tree that proves that Black is
winning. (And only counts the entropy for the black moves, not the white
moves in that tree). Once ahead, of course it can do whatever it wants that
preserves the win.

On Sun, Aug 6, 2017 at 10:31 AM, David Wu  wrote:

> * A little but not really.
> * No, and as far as we can tell, never. Even 7x7 is not rigorously solved.
> * Unknown.
> * Against Go-God (plays move that maximizes score margin, breaking ties by
> some measure of the entropy needed to build the proof tree relative to a
> human-pro-level policy net), I guess upper bound at 5 stones, likely less.
> Against Go-Devil (plays moves that maximizes win chance against *you*, has
> omniscient knowledge of all your weaknesses and perfect ability to forecast
> when you would have a brainfart and blunder), unknown, probably a bit more
> than vs Go-God.
>
> On Sun, Aug 6, 2017 at 9:49 AM, Cai Gengyang 
> wrote:
>
>> Is Alphago brute force search?
>> Is it possible to solve Go for 19x19 ?
>> And what does perfect play in Go look like?
>> How far are current top pros from perfect play?
>>
>> Gengyang
>>
>> ___
>> Computer-go mailing list
>> Computer-go@computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
>>
>
>
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Re: [Computer-go] Alphago and solving Go

[Vincent Richard]

*Is Alphago **brute **force search? *

No, simply because there are way to many possibilities in the game, 
roughly (19x19)!


Alphago tries to consider the game like the human do: it evaluates the 
board from only a limited set of moves, based on its "instinct". This 
instinct is generated from deep convolutionnal neural network (see 
alphago's aper for details 
http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html?foxtrotcallback=true)


*Is it possible to solve Go for 19x19 ? *
If I remember correctly, I pretty sure a team has solved go for 5x5 
boards. I let you guess reaching 19x19 is quite impossible. It is often 
said there are ore games of go than atoms in the universe


*And what does perfect play in Go look like? *
=> Alphago is currently the best players, hence the closest to a perfect 
play. Google Deepmind has published 50 self played games alphago 
https://deepmind.com/research/alphago/alphago-vs-alphago-self-play-games/. 
Some reviews by Michael Redmond have been published on youtube: 
https://www.youtube.com/watch?v=vjsN9BRInys


*How far are current top pros from perfect play?*
=> Difficult to say, even if Alphago is strong, some pros feel it still 
does some mistakes.



Vincent Richard


Le 06-Aug-17 à 10:49 PM, Cai Gengyang a écrit :

Is Alphago brute force search?
Is it possible to solve Go for 19x19 ?
And what does perfect play in Go look like?
How far are current top pros from perfect play?

Gengyang


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Re: [Computer-go] Alphago and solving Go

[David Wu]

* A little but not really.
* No, and as far as we can tell, never. Even 7x7 is not rigorously solved.
* Unknown.
* Against Go-God (plays move that maximizes score margin, breaking ties by
some measure of the entropy needed to build the proof tree relative to a
human-pro-level policy net), I guess upper bound at 5 stones, likely less.
Against Go-Devil (plays moves that maximizes win chance against *you*, has
omniscient knowledge of all your weaknesses and perfect ability to forecast
when you would have a brainfart and blunder), unknown, probably a bit more
than vs Go-God.

On Sun, Aug 6, 2017 at 9:49 AM, Cai Gengyang  wrote:

> Is Alphago brute force search?
> Is it possible to solve Go for 19x19 ?
> And what does perfect play in Go look like?
> How far are current top pros from perfect play?
>
> Gengyang
>
> ___
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>
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Re: [Computer-go] Alphago and solving Go

[Steven Clark]

No (have you read any of the papers about it?)
No
We don't know
We don't know (pros used to claim they were 2-3 stones away from God, but
AlphaGo might have encouraged them to be a bit more humble)

On Sun, Aug 6, 2017 at 9:49 AM, Cai Gengyang  wrote:

> Is Alphago brute force search?
> Is it possible to solve Go for 19x19 ?
> And what does perfect play in Go look like?
> How far are current top pros from perfect play?
>
> Gengyang
>
> ___
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Re: [Computer-go] Alphago and solving Go

[Álvaro Begué]

No, it is not possible to solve go on a 19x19 board. The closest we have is
5x5, I believe. We have a pretty good idea what optimal play looks like on
7x7. The difficulty of finding optimal play on large boards is
unfathomable.

Álvaro.


On Sun, Aug 6, 2017 at 10:06 AM Cai Gengyang  wrote:

> Is Alphago brute force search?
> Is it possible to solve Go for 19x19 ?
> And what does perfect play in Go look like?
> How far are current top pros from perfect play?
>
> Gengyang
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[Computer-go] Alphago and solving Go

[Cai Gengyang]

Is Alphago brute force search?
Is it possible to solve Go for 19x19 ?
And what does perfect play in Go look like?
How far are current top pros from perfect play?

Gengyang
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